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A novel person-reported measure of safety-seeking behaviours: a preliminary study in older victims of community crime

Published online by Cambridge University Press:  28 November 2025

Jessica Satchell
Affiliation:
Division of Psychiatry, University College London , London, UK
Gary Brown
Affiliation:
Department of Psychology, Royal Holloway, London, UK
Chris R. Brewin
Affiliation:
Division of Psychology and Language Sciences, University College London, London, UK
Jo Billings
Affiliation:
Division of Psychiatry, University College London , London, UK
Gerard Leavey
Affiliation:
Bamford Centre for Mental Health and Wellbeing, Ulster University, Belfast, UK
Marc Antony Serfaty*
Affiliation:
Division of Psychiatry, University College London , London, UK The Priory Hospital North London, London, UK
*
Corresponding author: Marc Antony Serfaty; Email: m.serfaty@ucl.ac.uk
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Abstract

Background:

Community crime against older people is of increasing concern but the relationship between safety-seeking behaviours and continued psychological distress has not been examined. As existing assessment tools have limited validity, we aimed to investigate this by designing a novel person-reported safety-seeking behaviour measure (PRSBM) and conducting preliminary evaluation of its wider applicability.

Method:

We collected mixed-methods data from n=100 initially distressed older victims at 3 months post-crime, using the PRSBM. This asked older victims how often they engaged in six behaviours (checking, reassurance-seeking, rumination, avoidance, rituals, hypervigilance), what these were, how often, and how much they had changed since the crime. We measured continued distress using the two-item General Anxiety Disorder and Patient Health Questionnaires. We analysed qualitative behaviour data using codebook thematic analysis, quantitative data on behaviour frequency and change using logistic regression adjusted for gender, age and crime type, and explored the PRSBM psychometric structure using unique variable analysis.

Results:

Older victims reported a wide range of safety-seeking behaviours conceptually consistent with their experiences. Some were highly restrictive; others may help maintain independence. The frequency of checking, avoidance, and hypervigilance, and a change in avoidance, were most strongly associated with continued distress. The PRSBM was acceptable, comprehensive, and captured differences and commonalities in safety-seeking.

Conclusions:

As older victims identified as avoidant appear at risk of losing their independence, referral for treatment is recommended. The PRSBM appears promising as a research and clinical tool in a range of settings, suggesting further testing in different populations would be worthwhile.

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Creative Commons
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of British Association for Behavioural and Cognitive Psychotherapies

Introduction

Concerns are growing about the psychological impact of community crime in older victims (Burnes et al., Reference Burnes, Henderson, Sheppard, Zhao, Pillemer and Lachs2017; Muhammad et al., Reference Muhammad, Meher and Sekher2021; Qin and Yan, Reference Qin and Yan2018). These are crimes committed by strangers or acquaintances (World Health Organisation, 2023), which have been overlooked in research compared with elder abuse and domestic violence (e.g. Knight and Hester, Reference Knight and Hester2016; Roberto and Hoyt, Reference Roberto and Hoyt2021; Yunus et al., Reference Yunus, Hairi and Choo2019). Whilst prevalence data are lacking, an estimated 26,541 community crimes were reported by older victims aged 65 and over in 2022–2023 in London (UK) alone (M.P.S. personal communication, 2023) and, given that 60–70% of crimes go unreported (Buil-Guil et al., Reference Buil-Gil, Medina and Shlomo2021; MacDonald, Reference MacDonald2002), the true figure may be even higher. Whilst many older victims of community crime cope well, the consequences for others can be debilitating (HMICFRS, 2019; Thornton et al., Reference Thornton, Hatton, Malone, Fryer, Walker, Cunningham and Durrani2003). An estimated 28% of older victims of different crime types continue to suffer depression and/or anxiety 3 months later (Serfaty et al., Reference Serfaty, Ridgewell, Drennan, Kessel, Brewin, Leavey, Wright, Laycock and Blanchard2016), which is considerably higher than rates of depression (7%) and anxiety (4%) in older people globally (World Health Organisation, 2017). Burglary (odds ratio [OR]: 2.4) and violent crime (OR: 2.1) have been associated with accelerated mortality and increased risk of nursing-home placement (Donaldson, Reference Donaldson2003; Lachs et al., Reference Lachs, Bachman, Williams, Kossack, Bove and O’Leary2006). There are also reports of older victims changing their behaviour after a crime; including avoiding online banking (Tripathi et al., Reference Tripathi, Robertson and Cooper2019), increasing security (Qin and Yang, Reference Qin and Yan2018), and praying for protection (Satchell et al., Reference Satchell, Dalrymple, Leavey and Serfaty2023). It is unclear whether these changes reflect healthy coping or safety-seeking behaviours, which are potentially maladaptive.

Safety-seeking behaviours are unnecessary or dysfunctional overt or covert actions intended to prevent, escape, or reduce risk or severity of potential threats (Salkovskis, Reference Salkovskis1991; Telch and Lancaster, Reference Telch, Lancaster, Neudeck and Wittchen2012). This may range from outright avoidance to subtle behaviours like checking (Hoffman and Chu, Reference Hoffman and Chu2019) and may be cognitive or behavioural (Rachman, Reference Rachman1997). Their presentation varies based on the individual’s specific concerns (Goetz et al., Reference Goetz, Davine, Siwiec and Lee2016).

Safety-seeking behaviours are dysfunctional because they maintain threat perception (Salkovskis, Reference Salkovskis1991). When feared outcomes do not occur, individuals may attribute this to their behaviour instead of the lack of danger (Lovibond et al., Reference Lovibond, Saunders, Weidemann and Mitchell2008). They may make anxiety tolerable, preventing opportunities for habituation (Sharpe et al., Reference Sharpe, Todd, Scott, Gatzounis, Menzies and Meulders2022), and even make feared outcomes more likely (McManus et al., Reference McManus, Sacadura and Clark2008). For example, excessively checking locks when leaving home may draw attention to it being vacated. Of the few crime victim studies, safety-seeking behaviours were significantly associated with post-traumatic stress disorder (PTSD) in middle-aged physical and sexual assault victims (Dunmore et al., Reference Dunmore, Clark and Ehlers1999; Dunmore et al., Reference Dunmore, Clark and Ehlers2001) and student and middle-aged trauma populations, including assault victims (Blakey et al., Reference Blakey, Kirby, McClure, Elbogen, Beckham, Watkins and Clapp2020). However, details on assessment were limited (Dunmore et al., Reference Dunmore, Clark and Ehlers1999; Dunmore et al., Reference Dunmore, Clark and Ehlers2001) or it was unreported whether measures had been pre-tested for suitability in this population (Blakey et al., Reference Blakey, Kirby, McClure, Elbogen, Beckham, Watkins and Clapp2020).

It has long-been recognised that improved assessment of safety-seeking behaviours is needed (Telch and Lancaster, Reference Telch, Lancaster, Neudeck and Wittchen2012). Most measures are for specific disorders, such as social anxiety (e.g. Cuming et al., Reference Cuming, Rapee, Kemp, Abbott, Peters and Gaston2009) or phobias (e.g. Krause et al., Reference Krause, Macdonald, Goodwill, Vorstenbosch and Antony2018), limiting their applicability to other populations. They also list specific behaviours, pre-determined by researchers, which may not apply to that individual. For example, the Post-Traumatic Safety Behaviour Questionnaire (Blakey et al., Reference Blakey, Kirby, McClure, Elbogen, Beckham, Watkins and Clapp2020), which has been used with crime victims, has some items which may be relevant (e.g. ‘Require the presence of a ‘safe person’ in public places’) and some intended for other fears (e.g. ‘Carefully eliminate all distractions while driving’). Even measures specifically for crime victims (Dunmore et al., Reference Dunmore, Clark and Ehlers1999; Dunmore et al., Reference Dunmore, Clark and Ehlers2001) may not fully capture behaviours as some may ‘avoid unfamiliar places’, while others may go to unfamiliar places but only in daytime or avoid going out altogether. These measures also rate behaviours based on how often they are engaged in, rather than how much of a change this is since the crime (or index trauma), making it unclear whether these are new or pre-existing behaviours.

Existing measures therefore have limited content validity, especially in crime victims, and may perpetuate misconceptions that safety-seeking is defined by particular behaviours rather than their underlying functions (e.g. avoidance) (Hoffman and Chu, Reference Hoffman and Chu2019). As safety-seeking is idiosyncratic (Goetz et al., Reference Goetz, Davine, Siwiec and Lee2016), individuals should be asked what they consider to be threatening (Gústavsson et al., Reference Gústavsson, Salkovskis and Sigurðsson2021). Victims of crime or other adverse events should also be asked whether these behaviours are a change since the incident.

A person-reported approach to measuring safety-seeking behaviours

Mixed-methods assessment tools, such as patient-generated measures, may offer a solution to safety-seeking behaviour assessment as they incorporate personal perspective while generating data that can be compared across participants (Cox and Klinger, Reference Cox and Klinger2021; Regnault et al., Reference Regnault, Willgoss and Barbic2017). They ask respondents to qualitatively describe the problem most affecting them and then rate it on a scale (Paterson, Reference Paterson1996; Ruta et al., Reference Ruta, Garratt, Leng, Russell and MacDonald1994). For example, the Psychological Outcome Profiles (PSYCHLOPS; Ashworth et al., Reference Ashworth, Shepherd, Christey, Matthews, Wright, Parmentier, Robinson and Godfrey2004), asks respondents to: ‘Choose the problem that troubles you most’ (qualitative response) and ‘Score how much has it affected you over the last week’ (0: ‘not at all affected’ to 5: ‘severely affected’). The PSYCHLOPS is person-centred and has been found to have good acceptability amongst patients and therapists (Antunes et al., Reference Antunes, Sales and Elliott2020). It is sensitive to change (Ashworth et al., Reference Ashworth2007), and over two-thirds of reported items were not found on quantitative comparators (Sales et al., Reference Sales, Neves, Alves and Ashworth2018). However, work to establish its reliability and validity remains ongoing (Sales et al., Reference Sales, Faísca, Ashworth and Ayis2023).

Mixed-methods approaches are recommended when measuring experiences uniquely known to the respondent (Black, Reference Black2013). However, questions remain about how best to develop and evaluate them, as not all conventional tests of psychometric quality are appropriate (Lyon et al., Reference Lyon, Connors, Jensen-Doss, Landes, Lewis, McLeod, Rutt, Stanick and Weiner2017; Sales et al., Reference Sales, Neves, Alves and Ashworth2018). For example, inter-rater reliability does not apply, as the measure relies on self-report rather than external observation. Similarly, the focus on subjective perspectives means they cannot be meaningfully compared with objective or independent assessments (Howell et al., Reference Howell, Amir, Guha, Manera and Tong2022). The assumption that they can assess the criterion variable equally well across the entire sample also remain untested. Furthermore, changes in how individuals understand or evaluate their own behaviour – such as during the course of therapy – can lead to response shifts, which may be misinterpreted as unreliability, when in fact they reflect the genuine therapeutic progress (van der Willik et al., Reference van der Willik, Terwee, Bos, Hemmelder, Jager, Zoccali, Dekker and Meuleman2021).

Despite these challenges, mixed-methods tools are well-suited to capturing individual behaviours, and health authorities internationally are increasingly advocating for wider use of experience-centred measures (OECD; 2024). Feasibility research is therefore needed to address this research gap (Ashworth et al., Reference Ashworth, Guerra and Kordowicz2019). Initial guidance recommends establishing content validity as the first critical step, to ensure the measure captures the concept of interest (Food and Drug Administration, 2009). This requires accumulating supportive quantitative and qualitative evidence to demonstrate relevance, clarity, and comprehensiveness (Cappelleri et al., Reference Cappelleri, Jason Lundy and Hays2014; Food and Drug Administration, 2009; Hawkins et al., Reference Hawkins, Elsworth and Osborne2018). Once achieved, subsequent evaluation can be considered, involving two phases: (1) feasibility via exploratory analyses and (2) confirmatory psychometrics to establish measurement characteristics (Cappelleri et al., Reference Cappelleri, Jason Lundy and Hays2014).

Aims

We firstly aimed to document safety-seeking behaviours used by older victims of community crime and quantitatively investigate whether these are associated with continued psychological distress at 3 months post-crime. To do this, we secondly aimed to design a novel person-reported safety-seeking behaviour measure (PRSBM) to collect mixed-methods data. As the potential benefits of a person-reported approach may extend beyond this population, we purposefully designed our measure to be broadly applicable to adverse events. Our third aim was therefore to use this sample to begin the first phase of feasibility evaluation by assessing the PRSBM’s content validity, usability and acceptability, and to explore its psychometric properties using a novel statistical technique (unique variable analysis).

Method

Setting

We nested our study within a randomised controlled trial of adapted cognitive behavioural therapy (CBT) for victims of community crime aged 65 and over in London (UK), in collaboration with the police and a mental health charity (for details, see Serfaty et al., Reference Serfaty, Satchell, Lee, Laycock, Brewin, Buszewicz, Leavey, Drennan, Vickerstaff, Cooke and Kessel2025). Older victims reporting a crime to the police were screened for distress symptoms within 2 months by community support officers using the 2-item Patient Health Questionnaire (PHQ-2) and Generalized Anxiety Disorder scale (GAD-2) (Kroenke et al., Reference Kroenke, Spitzer and Williams2003; Kroenke et al., Reference Kroenke, Spitzer, Williams, Monahan and Löwe2007). These officers collected sociodemographic data and obtained consent for data-sharing and contact from university researchers. Distressed older victims were followed up 3 months later and reassessed on the PHQ-2/GAD-2 by researchers and, if still distressed, recruited into the trial. This study utilized the PHQ-2/GAD-2 data from the 3-month follow-up for initially distressed older victims, collected between June 2018 and September 2019.

Measures

We defined continued psychological distress as a score of 2 or more on the GAD-2 and/or 3 or more on the PHQ-2 at 3 months post-crime.

Patient Health Questionnaire-2

The PHQ-2 (Kroenke et al., Reference Kroenke, Spitzer and Williams2003) is a rapid and reliable screening tool for depression, which assesses two symptoms over the preceding 2 weeks: (1) Feeling down, depressed, or hopeless and (2) Little interest or pleasure in doing things. Items are scored on 4-point scales (0–3), giving a maximum score of 6, with a score of 3 or more indicating depression. The PHQ-2 is reported to have excellent discriminant validity, and acceptable sensitivity and specificity (Staples et al., Reference Staples, Dear, Gandy, Fogliati, Fogliati, Karin, Nielssen and Titov2019) and internal consistency (α=0.79) (Bisby et al., Reference Bisby, Karin, Scott, Dudeney, Fisher, Gandy, Hathway, Heriseanu, Staples, Titov and Dear2022). It has been validated in older adults (Li et al., Reference Li, Friedman, Conwell and Fiscella2007) and previously used in older victims (Satchell et al., Reference Satchell, Dalrymple, Leavey and Serfaty2023; Serfaty et al., Reference Serfaty, Ridgewell, Drennan, Kessel, Brewin, Leavey, Wright, Laycock and Blanchard2016).

Generalized Anxiety Disorder-2

The GAD-2 (Kroenke et al., Reference Kroenke, Spitzer, Williams, Monahan and Löwe2007), abbreviated from the GAD-7, corresponds with DSM-IV generalized anxiety criteria (Spitzer et al., Reference Spitzer, Kroenke, Williams and Löwe2006). It screens two anxiety symptoms over the preceding 2 weeks: (1) Feeling nervous, anxious, or on edge and (2) Not being able to stop or control worrying. Items are scored on 4-point scales (0–3), giving a maximum score of 6. A score of 3 or more indicates anxiety in most populations, but a lower cut-off of 2 or more is recommended for older adults (Wild et al., Reference Wild, Eckl, Herzog, Niehoff, Lechner, Maatouk, Schellberg, Brenner, Müller and Löwe2014). Meta-analyses found that the GAD-2 has acceptable psychometric properties (Plummer et al., Reference Plummer, Manea, Trepel and McMillan2016), including internal consistency (α=0.84) (Bisby et al., 2022). It has been validated in older adults (Wild et al., Reference Wild, Eckl, Herzog, Niehoff, Lechner, Maatouk, Schellberg, Brenner, Müller and Löwe2014) and previously used in older victims (Satchell et al., Reference Satchell, Dalrymple, Leavey and Serfaty2023; Serfaty et al., Reference Serfaty, Ridgewell, Drennan, Kessel, Brewin, Leavey, Wright, Laycock and Blanchard2016).

Person-Reported Safety-seeking Behaviour Measure (PRSBM)

Our novel PRSBM (see Supplementary material) enquires about six safety-seeking behaviour functions: (1) Checking (There are things that people may check regularly), (2) Reassurance-Seeking (People may look for information or ask other people for their opinions to help them judge whether they are safe), (3) Rumination (People may go over in their mind how they can prevent a similar situation from happening again), (4) Avoidance (There are certain situations, places, people, or thoughts that people may avoid), (5) Rituals (If people suddenly think about something bad happening to them, there may be little things that they think, say, or do to help them feel better again), and (6) Hypervigilance (Some people may be alert for people or situations that could be a threat to them).

For each behaviour, respondents were asked at 3-month follow-up whether, since the incident: (A) This is something that I do (yes/no) and, if yes: (B) The thing I do the most is … (qualitative response). The qualitative responses are rated by respondents on two 7-point Likert scales: (C) Frequency: I do this (–3: ‘never’ to +3: ‘all the time’), and (D) Change: For me, this is … ((–3: ‘a lot less than before’ to +3: ‘a lot more than before’). The Frequency scale assesses the extent that respondents currently engage in the behaviour whilst the Change scale assesses whether this is a change from before the crime. We sat with participants so they could see the measure and read the questions aloud, writing behaviours as described verbatim by them and marking the scale option they said or pointed to. We sought to minimise socially desirable responding by completing the PRSBM during home visits, providing a confidential and comfortable setting that facilitated rapport-building.

PRSBM development

We aimed to develop a measure broadly applicable to any adverse event so our questions referred to negative ‘situations’ or ‘incidents’ instead of ‘crime’. The questions were informed by the existing literature and our Trial Management Group, including psychologists, psychiatrists, gerontologists, criminologists, statisticians, and two older adults with lived experience of being crime victims. Our lived experience advisors also provided initial guidance on its usability and acceptability (INVOLVE, 2021), before pre-testing in n=31 older victims using cognitive interviewing (Food and Drug Administration, 2009; Willis, Reference Willis1999). Based on their feedback, we refined the measure until the language and instructions were clear and acceptable.

Full details of amendments from pre-testing are available on request. Briefly, we found the PRSBM worked well when researcher assisted. Participants provided conceptually consistent responses to six safety-seeking behaviour functions (checking, reassurance-seeking, rumination, avoidance, rituals, hypervigilance). We tested two further questions (‘Doing things in a different way’ and ‘Any other examples’) but found that participants either repeated responses reported elsewhere or described single incidents which could not be meaningfully rated on the Frequency or Change scales (e.g. installing burglar alarms), so we dropped these from the measure. We tried asking participants to estimate how long they spent on each behaviour, which worked well for some (e.g. checking), but they struggled to meaningfully estimate how long they spent on avoidance, so we also dropped this. We tested different rating scale types and found that Likert scales were the most intuitive. Participants also recommended including ‘opt-out’ questions, so that they could skip behaviours which were irrelevant to them.

Qualitative analysis

We analysed person-reported responses using inductive codebook thematic analysis (Braun and Clarke, Reference Braun and Clarke2006; Braun and Clarke, Reference Braun and Clarke2022) in NVivo version 12.0 (2020). We read each response and assigned descriptive nodes. For example, ‘checking locks on doors’, and ‘doors are double-locked’ was coded ‘checking locks’ as they were conceptually similar. After describing items, we grouped similar items under parent nodes. For example, ‘checking locks’ and ‘checking lights are on when going out’ were categorised under ‘checking behaviours when leaving home’, while ‘checking windows have not been smashed’ and ‘checking jewellery is still there’ were grouped into ‘checking behaviours when returning home’. We then considered broader themes (e.g. ‘home security’).

Quantitative analysis

We explored the PRSBM’s psychometric structure using unique variable analysis (UVA; Christensen et al., Reference Christensen, Garrido and Golino2023) in R (R Core Team, 2023). UVA is a type of network analysis, which is useful for examining datasets where it is not yet known how variables relate to each other (Borsboom et al., Reference Borsboom, Deserno, Rhemtulla, Epskamp, Fried, McNally, Robinaugh, Perugini, Dalege, Costantini, Isvoranu, Wysocki, van Borkulo, van Bork and Waldorp2021). Items in a measure providing similar information tend to be highly correlated, so UVA compares every possible pairing of scales and presents these as figures called weighted topographical overlap (wTO). A wTO of 0.25 or over has been used to indicate ‘substantially high overlap’ in recent studies (Brown et al., Reference Brown, Delgadillo and Golino2023).

We tested associations between safety-seeking and continued distress in older victims at 3 months post-crime using logistic regression in SPSS version 27 (IBM Corporation, 2020). Our binary outcome variable was whether they scored as psychologically distressed or not on the PHQ-2/GAD-2 at 3 months post-crime. Our independent variables were the Frequency and Change scales for each behaviour on the PRSBM, which we treated as continuous (Robitzsch, Reference Robitzsch2020). Univariate logistic regression was used to test each item’s association with distress, adjusted for gender, age, and crime type. Variables significantly associated with distress (p<.05) after adjustment were selected for stepwise multi-variable logistic regression, using backwards elimination (p<.05), to compare their strength of association against each other. We tested Frequency and Change scales separately to minimize multi-collinearity.

Missing data

The developed PRSBM included a screening question ‘This is something that I do’ (yes/no) which instructed participants to skip irrelevant sub-items. We did not consider these as missing data as respondents were instructed to omit them. Those who selected ‘no’ were recoded on the frequency scales to –3 (never) and change scales to 0 (no more or no less than before), which was equivalent to not doing the behaviour, thereby preserving our sample size. We considered data missing if participants selected ‘yes’ but left scales blank. As the PRSBM was researcher-assisted, missing data were rare (3%), so we removed these cases through listwise deletion. As the GAD-2/PHQ-2 were also researcher-assisted and the PRSBM only conducted in those who had completed these, no outcome data were missing.

Results

We recruited N=100 initially distressed older victims, sample characteristics for which are presented in Table 1.

Table 1. Sample characteristics (N=100)

Checking was the most endorsed behaviour (n=87, 87%). Much of the sample also endorsed hypervigilance (83%), rumination (73%), avoidance (67%), and rituals (53%). Reassurance-seeking was the least common but was still endorsed by over a third (34%). As the data were not normally distributed, the averages for each scale are presented as medians and interquartile ranges (Table 2). Not all older victims engaged in all behaviours, but those who did tended to rate the frequency scales as ‘very often’ or ‘all the time’, and the change scales as ‘more than before’ or ‘a lot more than before’ the crime. The exception was rituals, which participants tended to rate that they did ‘very often’, but only ‘a little more’ than before the crime.

Table 2. Proportion who endorsed each behaviour, and distribution on the Frequency and Change scales of those who endorsed (N=100)

Frequency: ‘I do this’: never (–3), very rarely (–2), rarely (–1), half of the time (0), often (1), very often (2), all the time (3).

Change: ‘For me, this is…’: a lot less than before (–3), less than before (–2), a little less than before (–1), no more or no less than before (0), a little more than before (1), more than before (2), a lot more than before (3).

Qualitative findings

Older victims reported varied behaviours but all appeared conceptually related to the question (e.g. checking behaviours for the checking question) and to the crime they had experienced.

Checking

Participants often reported checking household security including locks, windows, burglar alarms, lights, or leaving televisions on to ‘give the appearance of someone at home’. Some checked when leaving the house or going to bed; others checked on their return for signs of damage, that no-one was hiding, or that valuables were still there. Older victims also checked their cars were locked, tyres ‘were not slashed’, or that mobile phones, keys, or purses were still with them. Some reported checking who was around or the identity of people calling them.

Reassurance-seeking

Older victims sought reassurance from friends, relatives, partners (e.g. ‘I call my family and friends when I cannot find my purse’), although one reported visiting their GP three times. This was to seek comfort, advice, ask whether loved ones were safe, or if others had had similar experiences. Some reported seeking-reassurance from the internet (e.g. crime reports) and another slept next to a panic alarm. One reported spending 6 hours daily watching home CCTV, and another reported her children had installed webcams to remotely monitor her safety.

Rumination

Rumination responses were broadly divided into thinking ‘what they should have done differently’ or ‘could do in the future’ including: risk assessments, imagining different scenarios, or mental reminders (e.g. Be careful opening emails). One older victim reported that keeping the crime on her mind helped her stay alert, while others found it offered self-reassurance (‘I think when I’m out “I’ve done my best to keep safe”’) or self-restraint (‘don’t escalate confrontation even when I’m right’).

Avoidance

Older victims avoided going out alone, after dark, and many reported not going out altogether (‘I haven’t been out for 3 or 4 weeks’). Others avoided staying home ‘in case somebody came’. Some avoided strangers, crowds, queues, certain people, ATMs, online banking, carrying cash, or answering the door or telephone. Others avoided specific places including shops, quiet streets, or high crime areas (e.g. ‘Central London’). Some reported an impact on their quality of life (‘we’ve stopped going on holiday’) or independence: (‘Places I used to go by myself’).

Rituals

Older victims reported faith practices (e.g. praying, attending church), good luck gestures (e.g. crossing fingers, crystals), or habits (e.g. smoking). Some reported cognitive rituals including distraction, thinking of happier times, or repeating messages (‘I tell myself “I’m being ridiculous”’).

Hypervigilance

Hypervigilance included looking for suspicious activity (e.g. ‘Looking out the window’), logging observations (e.g. Taking photographs), or reporting in neighbourhood WhatsApp groups. Some were hypervigilant in specific places (e.g. ‘public transport’) or situations (e.g. ‘When typing my PIN’). Some were hypervigilant at home, especially at night, including: staying awake, sleeping next to weapons, or getting up to check noises. Some reported holding their bags tightly or ‘asking people to knock a pre-specified amount of times so I know who is at the door’.

Quantitative analyses

Unique variable analysis

UVA found no overlap on any scale across behaviours (e.g. checking, avoidance), suggesting each contributed distinct information to the measure. However, the Frequency/Change scale pairs within each behaviour were found to have substantial overlap, with a wTO above 0.25 (Table 4). This overlap suggests that older victims who highly rated the Frequency scale for a behaviour also highly rated the corresponding Change scale, supporting that their behaviours were a change from before the crime.

Table 3. Summary overview of qualitative codes and themes in the PRSBM (N=100)

Table 4. Unique variable analysis

Logistic regression

We used variance inflation factor (VIF) to assess for multi-collinearity, which was found to be acceptable. However, as the UVA had identified high overlap on the Frequency/Change pairs for each behaviour, we adopted a cautious approach and tested the Frequency and Change scales separately. Analysis of residuals detected no outliers on the Frequency scales. On the Change scales, there was one for reassurance-seeking (Std. residual=–2.65), two for avoidance (Std. residual=3.23), two for rituals (Std residual=3.40), and one for hypervigilance (Std. residual=2.95). Visual inspections found that the same two participants reported engaging in behaviours a lot more than before the crime but scored negative for continued distress. The qualitative data showed one had lost their purse and now avoided carrying cash. Another had been burgled and reported not going out as much. These cases were removed through listwise deletion.

Univariate logistic regression (Table 5) found that reassurance-seeking and rumination were not significantly associated with continued distress on either scale. Rituals were not associated with continued distress on the Frequency scale but were on the Change scale. Checking, avoidance, and hypervigilance were each associated with continued distress on both scales. This suggests that for every point increase on these scales, the odds of continued distress were increased. The Change scales had especially high odds: 2.5 for rituals, 2.0 for avoidance, 1.91 for hypervigilance, and 1.70 for checking. Adjusting for gender, age, and crime type produced broadly similar estimates, suggesting associations were unaltered. The exceptions were avoidance ‘frequency’ (adjusted for gender) and rituals ‘change’ (adjusted for gender, age, and crime type), which became non-significant, so were dropped from further analyses.

Table 5. Univariate logistic regression testing safety-seeking behaviours and psychological distress (n=100)

a Univariable models for each safety-seeking behaviour using Frequency and Change scales; badjusted for gender; cadjusted for gender, age; dadjusted for gender, age, crime type. *Significant (p<.05), **significant (p<.01). UOR, unadjusted odds ratio; AOR, adjusted odds ratio.

For the multi-variable model, we carried forward variables significant at p<.05 on the fully-adjusted univariable logistic regression: checking (frequency, change), avoidance (change), and hypervigilance (frequency, change) with backwards elimination at p<.05 (Table 6). Backwards elimination removed both scales for checking and hypervigilance, but retained avoidance (change), suggesting a change in avoidance was most strongly associated with continued distress.

Table 6. Multi-variate logistic regression using backwards elimination (p<.05)

** Significant (p<.01).

Discussion

We are the first to develop a person-reported safety-seeking behaviour measure (PRSBM), designed with broad applicability to adverse events, and to test its sensitivity in a preliminary sample of older victims. Using robust mixed-methods, we collected rich participant-derived behaviour data and examined associations with continued psychological distress in older victims.

PRSBM

Older victims’ qualitative responses were wide-ranging but overall, their behaviours conceptually corresponded with the crimes they had experienced. Many suffered property-related crimes, so there were common themes (e.g. lock checking) as well as differences. For example, ‘checking the car’ meant locks for one participant and that tyres had ‘not been slashed’ for another. This reinforces that safety-seeking reflects individuals’ core concerns (Goetz et al., Reference Goetz, Davine, Siwiec and Lee2016; Gústavsson et al., Reference Gústavsson, Salkovskis and Sigurðsson2021) and affirms that qualitative components to safety-seeking behaviour assessment are important. This highlights the limitations of existing measures, which use items prescribed by researchers.

Despite the breadth of responses, behaviours were compatible with the questions (i.e. checking behaviours reported for the checking question). This supports that safety-seeking behaviours are about their underlying function, rather than outward presentation (Hoffman and Chu, Reference Hoffman and Chu2019), and suggests our measure was sensitive to these. Although we pre-tested a version of the PRSBM which asked participants whether they had ‘Any other examples’, we found this did not yield any new information, suggesting that the 6-item version (checking, reassurance-seeking, rumination, avoidance, rituals, hypervigilance) was sufficient. In summary, our 6-item PRSBM appeared able to comprehensively capture nuances and commonalities in safety-seeking behaviours.

Most older victims endorsed engaging in five of the behaviours (checking, rumination, avoidance, rituals, hypervigilance). Reassurance-seeking was least common but still endorsed by over a third. High protective behaviour rates have been reported previously (Qin and Yan, Reference Qin and Yan2018), but it was unclear whether these were specific to older victims or older people generally, or whether these were associated with distress. Whilst our cross-sectional study cannot definitively rule out whether behaviours reported on the PRSBM were pre-existing, we did ask whether they were a change since the crime. Reporting biases are possible as older victims knew they were being assessed for crime-related distress and may have attributed existing behaviours to this. Nonetheless, this is an important first step.

The unique variable analysis did not detect scale overlap across behaviours (e.g. checking, avoidance), suggesting each contributed distinct information to the PRSBM and should be retained. However, it detected substantial overlap for the ‘frequency’/‘change’ scale pairs within each behaviour, suggesting they were contributing similar information. This may indicate item redundancy, suggesting the PRSBM may be improved by removing one set of scales (Christensen et al., Reference Christensen, Garrido and Golino2023). Alternatively, as our sample had already been identified as initially distressed after the crime, the PRSBM may have been accurately detecting that those who engaged in the behaviours were at the extreme end of the scales. In other words, older victims appear to have been reporting that they engaged in the behaviour ‘frequentlyand that this was a ‘change’ since the crime. Had their behaviours been pre-existing, they might have been expected to score ‘frequency’ highly but not ‘change’. Rituals was the only behaviour which, of those who engaged, tended to be only ‘a little more’ than before, and the qualitative data suggest these may have been pre-existing behaviours (e.g. faith practices, smoking). Taken together, these finding suggest that the behaviours reported across all domains of the PRSBM (apart from rituals) are in response to the crime and are perceived by older victims as impacting their daily routines. The next step is to test the PRSBM in different samples to see whether different patterns of reporting emerge.

Safety-seeking behaviours in older victims

Avoidance change was most strongly associated with continued distress in older victims after adjusting for confounding. Avoidance is a well-established maintenance factor for anxiety, depression, and PTSD (Akbari et al., Reference Akbari, Mohammad, Hosseini, Kraft and Levin2022; Ehlers and Clark, Reference Ehlers and Clark2000; Moorey, Reference Moorey2010). This finding is important because it supports the PRSBM’s ability to capture this construct. Crucially, avoidance must be attributed to a trauma to count towards a PTSD diagnosis (DSM-V-TR; American Psychiatric Association, 2022) so finding avoidance ‘change’ was associated but not ‘frequency’ supports this. This expands evidence on safety-seeking behaviours after different crimes, as previous studies focused on student and working-age assault victims (Blakey et al., Reference Blakey, Kirby, McClure, Elbogen, Beckham, Watkins and Clapp2020; Dunmore et al., Reference Dunmore, Clark and Ehlers1999; Dunmore et al., Reference Dunmore, Clark and Ehlers2001).

Avoidance in older victims appeared restrictive and many reported avoiding leaving home entirely. Social isolation is associated with distress in older victims (Krause, Reference Krause1986; Reisig et al., Reference Reisig, Holtfreter and Turanovic2017). As older victims may be at increased risk of nursing home admission (Lachs et al., Reference Lachs, Bachman, Williams, Kossack, Bove and O’Leary2006), post-crime avoidance may also be a pathway for loss of independence. Two outliers from the residual analysis scored highly on avoidance change while scoring negative for continued distress; however, their symptoms may have ceased artificially by preventing anxiety increases (Helbig-Lang and Petermann, Reference Helbig-Lang and Petermann2010). While this might reflect healthy coping (Thwaites and Freeston, Reference Thwaites and Freeston2005), it seems unlikely given avoidance has consistently been associated with psychological sequelae (Akbari et al., Reference Akbari, Mohammad, Hosseini, Kraft and Levin2022).

Checking behaviours appeared consistent with safety-seeking but it was unclear from the qualitative data alone whether they were helpful or dysfunctional (Thwaites and Freeston, Reference Thwaites and Freeston2005). Checking on its own was associated with distress, suggesting behaviours may have been maladaptive, but the association was weaker when compared with avoidance. This may be because checking is considered a subtle form of avoidance (Sharpe et al., Reference Sharpe, Todd, Scott, Gatzounis, Menzies and Meulders2022). It can make anxiety tolerable, so the individual does not engage in complete avoidance, thereby providing opportunities for threat exposure and belief disconfirmation (Rachman et al., Reference Rachman, Radomsky and Shafran2008). For example, excessive lock checking may be unhelpful for older victims in isolation, but if it makes the difference between them leaving home or not, the overall impact may be less detrimental.

Hypervigilance on its own was similarly associated with distress but less than avoidance. Some older victims were avoidant and hypervigilant, such as the participant who spent all day monitoring home CCTV. Others were hypervigilant in places like the bank or public transport, suggesting it helped them not to avoid these situations. Studies suggest that hypervigilant individuals may be high or low in avoidance (Cisler and Koster, Reference Cisler and Koster2010; Kimble and Hyatt, Reference Kimble and Hyatt2019). Some older victims reported intentionally staying awake at night or sleeping next to weapons, suggesting they did not feel safe at home. Crimes like burglary may be especially distressing in older people as it is an invasion of somewhere that should feel secure and comforting (Delisi et al., Reference Delisi, Jones-Johnson, Johnson and Hochstetler2014). This absence of a safe place suggests they may be in constant distress (Golovchanova et al., Reference Golovchanova, Evans, Hellfeldt, Andershed and Boersma2023).

Positive and negative religious coping has previously been reported in older victims (Satchell et al., Reference Satchell, Dalrymple, Leavey and Serfaty2023), but rituals may also include superstition, and habits like smoking. These might be expected to be pre-existing behaviours. It is therefore interesting that the Change scale had even higher odds for continued distress than avoidance. However, the confidence intervals were wide, and the effect did not remain significant when adjusted for crime type. There may have been different patterns of reporting between those who always engaged in rituals and those that considered it a change since the crime, but we may have had insufficient power to detect this. It would be worthwhile replicating this with larger samples given its potentially substantial impact.

Neither reassurance-seeking nor rumination were associated with continued distress on either scale, in contrast with many studies reporting associations with anxious and depressive disorders (Brewin and Ehlers, Reference Brewin, Ehlers, Kahana and Wagnerin press; Halldorsson and Salkovskis, Reference Halldorsson and Salkovskis2023; Manrique-Millones et al., Reference Manrique-Millones, Garcia-Serna, Castillo-Blanco, Fernandez-Rios, Lizarzaburu-Aguinaga, Parihuaman-Quinde and Villarreal-Zegarra2023; Sharpe et al., Reference Sharpe, Todd, Scott, Gatzounis, Menzies and Meulders2022). Genuine associations may have been missed through inadequate power or construct validity, suggesting further testing in larger samples or refinement of the question may be needed.

Implications for policy and practice

Existing research in older victims of community crime has predominantly focused on fraud (Tripathi et al., Reference Tripathi, Robertson and Cooper2019), cybercrime (Havers et al., Reference Havers, Tripathi, Burton, Martin and Cooper2024a; Reference Havers, Tripathi, Burton, McManus and Cooper2024b) or violence (Muhammad et al., Reference Muhammad, Meher and Sekher2021), with relatively little on burglary or ‘high volume, low severity’ crimes like petty theft (Satchell et al., Reference Satchell, Craston, Drennan, Billings and Serfaty2022). We found a range of crimes psychologically impact older victims. This has important implications for criminal justice services, which under-serve older people (Brown and Gordon, Reference Brown and Gordon2022; HMICFRS, 2019). Police in the UK use the Cambridge Crime Harm Index to inform resource allocation (Sherman et al., Reference Sherman, Neyroud and Neyroud2016). This low-cost standardised metric weights how harmful each crime is relative to other crimes based on minimum sentencing guidelines (Van Ruitenburg and Ruiter, Reference Van Ruitenburg and Ruiter2023), which does not consider individual differences in coping in its assessment.

It is important that support is available for older victims, many of whom reported avoiding leaving home, as they appear at risk of losing independence. Maintaining autonomy in older adults is essential for health, quality of life, and preventing cognitive deterioration (Sanchez-Garcia et al., Reference Sanchez-Garcia, Garcia-Pena, Ramirez-Garcia, Moreno-Tamayo and Cantu-Quintanilla2019). It also helps reduce frailty and loneliness, both of which significantly increase risk of nursing home admission (Hoogendijk et al., Reference Hoogendijk, Afilalo, Ensrud, Kowal, Onder and Fried2019). As older victims have previously been found to be at increased risk (Lachs et al., Reference Lachs, Bachman, Williams, Kossack, Bove and O’Leary2006), this suggests a major preventable public health problem. Support is also needed for older victims hypervigilant within their homes, who may have minimal relief from their symptoms.

Disengaging from safety-seeking is a target of CBT, but it is debated whether all behaviours should be eliminated or whether some are beneficial (Rachman et al., Reference Rachman, Radomsky and Shafran2008). Whilst subtle behaviours like checking were individually associated with continued distress, they may be important for maintaining functioning in older victims. A phased approach targeting total avoidance of situations before other behaviours may reduce unintended consequences or attrition from therapy (Rachman et al., Reference Rachman, Radomsky and Shafran2008). Despite widely held concerns about the suitability of CBT in older adults (Frost et al., Reference Frost, Beattie, Bhanu, Walters and Ben-Shlomo2019), there is strong evidence supporting its efficacy (Werson et al., Reference Werson, Meiser-Stedman and Laidlaw2022).

Our PRSBM may aid therapeutic discussions around safety-seeking behaviours in older victims. We also purposely kept the wording general (e.g. ‘situation’ rather than ‘crime’) to widen its applicability to other incidents (e.g. bereavement, injury). Whilst further testing and replication of our findings in other populations is needed, our findings are proof-of-principle that the PRSBM has the potential to be useful in a range of clinical and research settings.

Strengths and limitations

We are the first to examine safety-seeking behaviours in older victims and to attempt a person-reported approach to measurement, with broad applicability. Our researcher-assisted PRSBM appears acceptable, comprehensive, person-centred, and able to capture idiosyncrasies and commonalities in behaviours. Inclusion of qualitative data also aided quantitative interpretation. Our findings on avoidance, checking and hypervigilance suggest good construct validity, and the overall compatibility of qualitative responses suggests good content validity, which is considered the most important patient-reported outcome measure (PROM) psychometric property (Food and Drug Administration, 2009).

The PRSBM’s limitations reflect wider challenges with evaluating PROMs as there are few sources of external validation (Lyon et al., Reference Lyon, Connors, Jensen-Doss, Landes, Lewis, McLeod, Rutt, Stanick and Weiner2017; Sales et al., Reference Sales, Neves, Alves and Ashworth2018). As behaviours were subjective, we could not assess convergent or discriminant validity, test–retest, or inter-rater reliability. We did not evaluate internal consistency as there was no a priori reason to suggest behaviours were correlated, which we considered a separate research question. However, we could have compared the PRSBM with clinician assessments of safety-seeking behaviours, which could be addressed in further research. As PROMs should be evaluated through accumulative evidence (Cappelleri et al., Reference Cappelleri, Jason Lundy and Hays2014; Hawkins et al., Reference Hawkins, Elsworth and Osborne2018), further development would be worthwhile.

Embedding our study within a trial collaborating with the police enabled us to recruit older victims within a defined time point. Previous studies have struggled with systematic identification of older victims and assessed the impact of crimes from many years previously (e.g. Acierno et al., Reference Acierno, Lawyer, Rheingold, Kilpatrick, Resnick and Saunders2007; Fredriksen-Goldsen et al., Reference Fredriksen-Goldsen, Cook-Daniels, Kim, Erosheva, Emlet, Hoy-Ellis, Goldsen and Muraco2014). However, we could not include sexual violence, which may be especially distressing in older victims (Bows, Reference Bows, Horvath and Brown2022), or unreported crimes. As 60–70% of crimes are unreported (Buil-Gil et al., Reference Buil-Gil, Medina and Shlomo2021), especially by victims from ethnic minorities or with complex care needs (Jones and Elliott, Reference Jones and Elliott2018; McCart et al., Reference McCart, Smith and Sawyer2010), our findings may not be representative of all older victims. Our London-based sample may also not be generalisable to older victims from elsewhere in the UK (Gordon and Brown, Reference Gordon, Brown, Newman and Gordon2023).

Cross-sectional designs do not permit inferences around causality or direction of the association. Combining the GAD-2/PHQ-2 also made it unclear whether associations were with depression, anxiety, or both. The screening tools were not diagnostic, although they strongly correlate with the GAD-7 and PHQ-9, which correspond with DSM-IV criteria for anxiety and depression (Kroenke et al., Reference Kroenke, Spitzer and Williams2001; Spitzer et al., Reference Spitzer, Kroenke, Williams and Löwe2006). We did not include PTSD because there is limited empirical data on which crimes may elicit traumatic stress responses in older people, who may differ from younger samples (Brewin et al., Reference Brewin, Andrews and Valentine2000; Jongedijk et al., Reference Jongedijk, van Vreeswijk, Knipscheer, Kleber and Boelen2022). We considered psychological distress more relevant as it encapsulates broader emotional responding after any community crime. Finally, although we adjusted for gender, age and crime type, we could have considered other variables such as previous mental health problems, whether participants lived alone, or whether the perpetrator had been arrested. These are all recommendations for future research.

Further development

A Delphi study on the safety-seeking behaviours currently included in the PRSBM and whether other items (e.g. carefulness) should be added is firstly recommended. Secondly, further testing in both older victims and other adverse event populations across diverse age groups and with larger sample sizes is needed. Factor analysis may help identify whether items can be combined or removed by assessing variable loading patterns. Comparison of the PRSBM with clinical assessment of safety-seeking behaviours may support its validity, and exploring whether different behaviours align to show high internal reliability would be worthwhile. Finally, recall bias could be addressed using alternative methods such as experience sampling methodology, which would facilitate self-evaluation of safety-seeking behaviours as they occur in daily life (Myin-Germeys et al., Reference Myin-Germeys, Kasanova, Vaessen, Vachon, Kirtley, Viechtbauer and Reininghaus2018; Oren-Yagoda et al., Reference Oren-Yagoda, Oren and Aderka2024).

Conclusions

Despite the prominence of safety-seeking behaviours to CBT, valid assessment tools have been lacking. Preliminary evaluation of the PRSBM suggests this is a promising solution. Older victims have been overlooked in safety-seeking behaviour research, yet our findings suggest that post-crime avoidance may have a profound impact on their subsequent functioning. Further research is needed to clarify what support may be helpful. The PRSBM may be a useful screening tool to help therapists target problematic safety-seeking behaviours in older victims and broader populations, and further development is recommended.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/S1352465825101197

Data availability statement

The data that support these findings are freely available from the UCL Research Data Repository. Quantitative dataset: https://doi.org/10.5522/04/25189082.v1; qualitative dataset: https://doi.org/10.5522/04/25188935.v1.

Acknowledgements

We wish to thank the VIP Trial Management Group for their feedback on the PRSBM including Vari Drennan, Gloria Laycock, Marta Buszewicz, Martin Blanchard, Anthony Kessel, and our PPI members. We are also thankful to Gemma Lewis, Vicki Vickerstaff and Rebecca Jones for providing additional statistical advice, and to the participants who pre-tested the PRSBM and took part in the current study.

Author contributions

Jessica Satchell: Conceptualization (lead), Data curation (lead), Formal analysis (lead), Investigation (lead), Methodology (equal), Project administration (lead), Writing - original draft (lead), Writing - review & editing (lead); Gary Brown: Formal analysis (lead), Supervision (supporting), Writing - review & editing (supporting); Chris Brewin: Conceptualization (equal), Funding acquisition (supporting), Methodology (supporting), Supervision (supporting), Writing - review & editing (supporting); Jo Billings: Formal analysis (supporting), Methodology (supporting), Supervision (equal), Writing - review & editing (supporting); Gerry Leavey: Funding acquisition (supporting), Supervision (supporting), Writing - review & editing (supporting); Marc Antony Serfaty: Conceptualization (supporting), Formal analysis (supporting), Funding acquisition (lead), Investigation (supporting), Methodology (supporting), Resources (lead), Supervision (lead), Writing - review & editing (supporting).

Financial support

The VIP Trial is funded by the National Institute for Health and Care Research (NIHR) Public Health Research (grant: 13/164/32). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role other than financial support.

Competing interests

Author Dr Gary Brown was an associate editor for Behavioural and Cognitive Psychotherapy but was not involved in the decision whether to accept this manuscript for publication.

Ethical standards

Our study was approved by the University College London Research Ethics Committee (6960/001). We obtained informed consent for all participants and adhered to the Declaration of Helsinki.

References

Acierno, R., Lawyer, S. R., Rheingold, A., Kilpatrick, D. G., Resnick, H. S., & Saunders, B. E. (2007). Current psychopathology in previously assaulted older adults. Journal of Interpersonal Violence, 22, 250258. https://doi.org/10.1177/0886260506295369 CrossRefGoogle ScholarPubMed
Akbari, M., Mohammad, S., Hosseini, Z., Kraft, J., & Levin, M. (2022). Experiential avoidance in depression, anxiety, obsessive-compulsive related, and posttraumatic stress disorders: a comprehensive systematic review and meta-analysis. Journal of Contextual Behavioural Science 24, 6578. https://doi.org/10.1016/j.jcbs.2022.03.007 Google Scholar
American Psychiatric Association (2022). Diagnostic and Statistical Manual of Mental Disorders (5th edn, text rev.). https://doi.org/10.1176/appi.books.9780890425787 CrossRefGoogle Scholar
Antunes, R. P., Sales, C. M. D., & Elliott, R. (2020). The clinical utility of the Personal Questionnaire (PQ): a mixed methods study. Counselling Psychology Quarterly, 33(1), 2545. https://doi.org/10.1080/09515070.2018.1439451 CrossRefGoogle Scholar
Ashworth, M. (2007). PSYCHLOPS – a psychometric outcome measure that is finding a niche. Counselling and Psychotherapy Research, 7, 201202. https://doi.org/10.1080/14733140701706870 CrossRefGoogle Scholar
Ashworth, M., Guerra, D., & Kordowicz, M. (2019). Individualised or standardised outcome measures: a co-habitation? Administration and Policy in Mental Health and Mental Health Services Research, 46, 425428. https://doi.org/10.1007/s10488-019-00928-z CrossRefGoogle ScholarPubMed
Ashworth, M., Shepherd, M., Christey, J., Matthews, V., Wright, K., Parmentier, H., Robinson, S., & Godfrey, E. (2004). A client-generated psychometric instrument: the development of ‘PSYCHLOPS’. Counselling and Psychotherapy Research, 4, 2731. https://doi.org/10.1080/14733140412331383913 CrossRefGoogle Scholar
Bisby, M. A., Karin, E., Scott, A. J., Dudeney, J., Fisher, A., Gandy, M., Hathway, T., Heriseanu, A. I., Staples, L., Titov, N., & Dear, B. F. (2022). Examining the psychometric properties of brief screening measures of depression and anxiety in chronic pain: The Patient Health Questionnaire 2-item and Generalized Anxiety Disorder 2-item. Pain Practice, 22, 478486. https://doi.org/10.1111/papr.13107 CrossRefGoogle ScholarPubMed
Black, N. (2013). Patient reported outcome measures could help transform healthcare. BMJ, 346, f167. https://doi.org/10.1136/bmj.f167 CrossRefGoogle ScholarPubMed
Blakey, S. M., Kirby, A. C., McClure, K. E., Elbogen, E. B., Beckham, J. C., Watkins, L. L., & Clapp, J. D. (2020). Posttraumatic safety behaviors: characteristics and associations with symptom severity in two samples. Traumatology, 26, 7483. https://doi.org/10.1037/trm0000205 CrossRefGoogle Scholar
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1. https://doi.org/10.1038/s43586-021-00055-w CrossRefGoogle Scholar
Bows, H. (2022). Rape of older people In Horvath, M. A. H. & Brown, J. M. (eds), Rape: Challenging Contemporary Thinking - 10 Years On (pp. 93110). Routledge 10.4324/9781003163800-9CrossRefGoogle Scholar
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77101. https://doi.org/10.1191/1478088706qp063oa CrossRefGoogle Scholar
Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis Qualitative Psychology, 9, 326 https://doi.org/10.1037/qup0000196 CrossRefGoogle Scholar
Brewin, C. R., Andrews, B., & Valentine, J. D. (2000). Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. Journal of Consulting and Clinical Psychology, 68, 748766.10.1037/0022-006X.68.5.748CrossRefGoogle ScholarPubMed
Brewin, C. R., & Ehlers, A. (in press). Posttraumatic stress disorder. In Kahana, M. & Wagner, A. (eds), Handbook of Human Memory: Foundations and Applications. Oxford: OUP.Google Scholar
Brown, G. P., Delgadillo, J., & Golino, H. (2023). Distinguishing the dimensions of the original dysfunctional attitude scale in an archival clinical sample. Cognitive Therapy and Research, 47, 6983. https://doi.org/10.1007/s10608-022-10333-w CrossRefGoogle Scholar
Brown, K. J., & Gordon, F. (2022). Improving access to justice for older victims of crime by reimagining conceptions of vulnerability. Ageing and Society, 42, 614631. https://doi.org/10.1017/s0144686x20001051 CrossRefGoogle Scholar
Buil-Gil, D., Medina, J., & Shlomo, N. (2021). Measuring the dark figure of crime in geographic areas: small area estimation from the Crime Survey for England and Wales. British Journal of Criminology, 61, 364388. https://doi.org/10.1093/bjc/azaa067 CrossRefGoogle Scholar
Burnes, D., Henderson, C. R., Sheppard, C., Zhao, R., Pillemer, K., & Lachs, M. S. (2017). Prevalence of financial fraud and scams among older adults in the United States: a systematic review and meta-analysis. American Journal of Public Health, 107, e13e21. https://doi.org/10.2105/ajph.2017.303821 CrossRefGoogle ScholarPubMed
Cappelleri, J. C., Jason Lundy, J., & Hays, R. D. (2014). Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures. Clinical Therapeutics, 36, 648662. https://doi.org/10.1016/j.clinthera.2014.04.006 CrossRefGoogle ScholarPubMed
Christensen, A. P., Garrido, L. E., & Golino, H. (2023). Unique variable analysis: a network psychometrics method to detect local dependence. Multivariate Behavioral Research, 118. https://doi.org/10.1080/00273171.2023.2194606 Google ScholarPubMed
Cisler, J. M., & Koster, E. H. (2010). Mechanisms of attentional biases towards threat in anxiety disorders: an integrative review. Clinical Psychology Review, 30, 203216. https://doi.org/10.1016/j.cpr.2009.11.003 CrossRefGoogle ScholarPubMed
Cox, W. M., & Klinger, E. (2021). Assessing current concerns and goals idiographically: a review of the motivational structure questionnaire family of instruments. Journal of Clinical Psychology, 79, 667682. https://doi.org/10.1002/jclp.23256 CrossRefGoogle ScholarPubMed
Cuming, S., Rapee, R. M., Kemp, N., Abbott, M. J., Peters, L., & Gaston, J. E. (2009). A self-report measure of subtle avoidance and safety behaviors relevant to social anxiety: development and psychometric properties. Journal of Anxiety Disorders, 23, 879883. https://doi.org/10.1016/j.janxdis.2009.05.002 CrossRefGoogle ScholarPubMed
Delisi, M., Jones-Johnson, G., Johnson, W. R., & Hochstetler, A. (2014). The aftermath of criminal victimization. Crime & Delinquency, 60, 85105. https://doi.org/10.1177/0011128709354036 CrossRefGoogle Scholar
Donaldson, R. (2003). Experiences of older burglary victims. Home Office Findings, 198, 14.Google Scholar
Dunmore, E., Clark, D. M., & Ehlers, A. (1999). Cognitive factors involved in the onset and maintenance of posttraumatic stress disorder (PTSD) after physical or sexual assault. Behaviour Research and Therapy, 37, 809829. https://doi.org/10.1016/s0005-7967(98)00181-8 CrossRefGoogle ScholarPubMed
Dunmore, E., Clark, D. M., & Ehlers, A. (2001). A prospective investigation of the role of cognitive factors in persistent posttraumatic stress disorder (PTSD) after physical or sexual assault. Behaviour Research and Therapy, 39, 10631084. https://doi.org/10.1016/s0005-7967(00)00088-7 CrossRefGoogle ScholarPubMed
Ehlers, A., & Clark, D. M. (2000). A cognitive model of posttraumatic stress disorder. Behaviour Research and Therapy, 38, 319345. https://doi.org/10.1016/s0005-7967(99)00123-0 CrossRefGoogle ScholarPubMed
Food and Drug Administration (2009). Patient reported outcome measures: use in medical product development to support labeling claims. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims Google Scholar
Fredriksen-Goldsen, K. I., Cook-Daniels, L., Kim, H.-J., Erosheva, E. A., Emlet, C. A., Hoy-Ellis, C. P., Goldsen, J., & Muraco, A. (2014). Physical and mental health of transgender older adults: an at-risk and underserved population. The Gerontologist, 54, 488500. https://doi.org/10.1093/geront/gnt021 CrossRefGoogle ScholarPubMed
Frost, R., Beattie, A., Bhanu, C., Walters, K., & Ben-Shlomo, Y. (2019). Management of depression and referral of older people to psychological therapies: a systematic review of qualitative studies. British Journal of General Practice, 69, e171e181. https://doi.org/10.3399/bjgp19X701297 CrossRefGoogle ScholarPubMed
Goetz, A. R., Davine, T. P., Siwiec, S. G., & Lee, H.-J. (2016). The functional value of preventive and restorative safety behaviors: a systematic review of the literature. Clinical Psychology Review, 44, 112124.10.1016/j.cpr.2015.12.005CrossRefGoogle ScholarPubMed
Golovchanova, N., Evans, B., Hellfeldt, K., Andershed, H., & Boersma, K. (2023). Older and feeling unsafe? Differences in underlying vulnerability, anxiety and life satisfaction among older adults. Aging and Mental Health, 27, 16361643. https://doi.org/10.1080/13607863.2023.2177255 CrossRefGoogle ScholarPubMed
Gordon, F., & Brown, K. (2023). Older victims, legal need and access to justice in rural communities in Northern Ireland. In Newman, D. & Gordon, F. (eds), Access to Justice in Rural Communities: Global Perspectives (pp. 101109).Google Scholar
Gústavsson, S. M., Salkovskis, P. M., & Sigurðsson, J. F. (2021). Cognitive analysis of specific threat beliefs and safety-seeking behaviours in generalised anxiety disorder: revisiting the cognitive theory of anxiety disorders. Behavioural and Cognitive Psychotherapy, 49, 526539. https://doi.org/10.1017/s135246582100014x CrossRefGoogle ScholarPubMed
Halldorsson, B., & Salkovskis, P. M. (2023). Reassurance and its alternatives: overview and cognitive behavioural conceptualisation. Journal of Obsessive-Compulsive and Related Disorders, 36. https://doi.org/10.1016/j.jocrd.2023.100783 CrossRefGoogle Scholar
Havers, B., Tripathi, K., Burton, A., Martin, W., & Cooper, C. (2024a). Exploring the factors preventing older adults from reporting cybercrime and seeking help: a qualitative, semistructured interview study. Health & Social Care in the Community. https://doi.org/10.1155/2024/1314265 CrossRefGoogle Scholar
Havers, B., Tripathi, K., Burton, A., McManus, S., & Cooper, C. (2024b). Cybercrime victimisation among older adults: A probability sample survey in England and Wales. PLOS One. https://doi.org/10.1016/j.janxdis.2024.102915. Epub 2024 Aug 16.CrossRefGoogle ScholarPubMed
Hawkins, M., Elsworth, G. R., & Osborne, R. H. (2018). Application of validity theory and methodology to Patient-reported outcome measures (PROMs): building an argument for validity. Quality of Life Research, 27, 16951710. https://doi.org/10.1007/s11136-018-1815-6 CrossRefGoogle ScholarPubMed
Helbig-Lang, S., & Petermann, F. (2010). Tolerate or eliminate? A systematic review on the effects of safety behavior across anxiety disorders. Clinical Psychology: Science and Practice, 17, 218233. https://doi.org/10.1111/j.1468-2850.2010.01213.x Google Scholar
HMICFRS (2019). The poor relation: the police and CPS response to crimes against older people. www.justiceinspectorates.gov.uk/hmicfrs Google Scholar
Hoffman, L. J., & Chu, B. C. (2019). When is seeking safety functional? Taking a pragmatic approach to distinguishing coping from safety. Cognitive and Behavioral Practice, 26, 176185. https://doi.org/10.1016/j.cbpra.2018.11.002 CrossRefGoogle Scholar
Hoogendijk, E. O., Afilalo, J., Ensrud, K. E., Kowal, P., Onder, G., & Fried, L. P. (2019). Frailty: implications for clinical practice and public health. The Lancet, 394, 13651375. https://doi.org/10.1016/s0140-6736(19)31786-6 CrossRefGoogle ScholarPubMed
Howell, M., Amir, N., Guha, C., Manera, K., & Tong, A. (2022). The critical role of mixed methods research in developing valid and reliable patient-reported outcome measures. Methods, 205, 213219. https://doi.org/10.1016/j.ymeth.2022.07.012 CrossRefGoogle ScholarPubMed
IBM Corporation (2020). IBM SPSS statistics for Windows (version 27.0) [computer software].Google Scholar
INVOLVE (2021). Briefing notes for researchers: public involvement in NHS, public health and social care research. INVOLVE, Eastleigh.Google Scholar
Jones, R., & Elliott, T. (2018). Capacity to give evidence in court: issues that may arise when a client with dementia is a victim of crime. Psychiatric Bulletin, 29, 324326. https://doi.org/10.1192/pb.29.9.324 CrossRefGoogle Scholar
Jongedijk, R. A., van Vreeswijk, M. F., Knipscheer, J. W., Kleber, R. J., & Boelen, P. A. (2022). The relevance of trauma and re-experiencing in PTSD, mood, and anxiety disorders. Journal of Loss and Trauma, 28, 404420. https://doi.org/10.1080/15325024.2022.2116782 CrossRefGoogle Scholar
Kimble, M. O. P., & Hyatt, A. B. (2019). Vigilance/avoidance to expected and presented stimuli in trauma survivors: an eye-tracking study. Journal of Trauma & Dissociation, 20, 228241. https://doi.org/10.1080/15299732.2019.1572041 CrossRefGoogle ScholarPubMed
Knight, L., & Hester, M. (2016). Domestic violence and mental health in older adults. International Review of Psychiatry, 28, 464474.10.1080/09540261.2016.1215294CrossRefGoogle ScholarPubMed
Krause, K. L., Macdonald, E. M., Goodwill, A. M., Vorstenbosch, V., & Antony, M. M. (2018). Assessing safety behaviors in fear of storms: validation of the storm-related safety behavior scale. Journal of Psychopathology and Behavioral Assessment, 40, 139148. https://doi.org/10.1007/s10862-017-9622-x CrossRefGoogle Scholar
Krause, N. (1986). Social support, stress, and well-being among older adults. Journal of Gerontology, 41, 512519. https://doi.org/10.1093/geronj/41.4.512 CrossRefGoogle ScholarPubMed
Kroenke, K., Spitzer, R., & Williams, J. (2003). The Patient Health Questionniare-2: validity of a two-item depression screener Medical Care, 41, 12841292. https://doi.org/10.2307/3768417 CrossRefGoogle Scholar
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine, 16(9), 606613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x CrossRefGoogle ScholarPubMed
Kroenke, K., Spitzer, R. L., Williams, J. B. W., Monahan, P. O., & Löwe, B. (2007). Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146, 317. https://doi.org/10.7326/0003-4819-146-5-200703060-00004 CrossRefGoogle ScholarPubMed
Lachs, M., Bachman, R., Williams, C. S., Kossack, A., Bove, C., & O’Leary, J. R. (2006). Violent crime victimization increases the risk of nursing home placement in older adults. The Gerontologist, 46, 583589. https://doi.org/10.1093/geront/46.5.583 CrossRefGoogle ScholarPubMed
Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007). Validity of the Patient Health Questionnaire-2 (PHQ-2) in identifying major depression in older people. Journal of the American Geriatrics Society, 55, 596602. https://doi.org/10.1111/j.1532-5415.2007.01103.x CrossRefGoogle ScholarPubMed
Lovibond, P. F., Saunders, J. C., Weidemann, G., & Mitchell, C. J. (2008). Evidence for expectancy as a mediator of avoidance and anxiety in a laboratory model of human avoidance learning. The Quarterly Journal of Experimental Psychology, 61, 11991216.10.1080/17470210701503229CrossRefGoogle Scholar
Lyon, A. R., Connors, E., Jensen-Doss, A., Landes, S. J., Lewis, C. C., McLeod, B. D., Rutt, C., Stanick, C., & Weiner, B. J. (2017). Intentional research design in implementation science: implications for the use of nomothetic and idiographic assessment. Translational Behavioral Medicine, 7, 567580. https://doi.org/10.1007/s13142-017-0464-6 CrossRefGoogle ScholarPubMed
MacDonald, Z. (2002). Official crime statistics: their use and interpretation. The Economic Journal, 112, F855106. https://doi.org/10.1111/1468-0297.00685 CrossRefGoogle Scholar
Manrique-Millones, D., Garcia-Serna, J., Castillo-Blanco, R., Fernandez-Rios, N., Lizarzaburu-Aguinaga, D. A., Parihuaman-Quinde, G. R., & Villarreal-Zegarra, D. (2023). When COVID-19 strikes mental health: a measurement analysis of reassurance seeking behavior scale in Peruvian population. Frontiers in Psychology, 14, 1132804. https://doi.org/10.3389/fpsyg.2023.1132804 CrossRefGoogle ScholarPubMed
McCart, M. R., Smith, D. W., & Sawyer, G. K. (2010). Help-seeking among victims of crime: a review of the empirical literature. Journal of Traumatic Stress, 23, 198206. https://doi.org/10.1002/jts.20509 CrossRefGoogle ScholarPubMed
McManus, F., Sacadura, C., & Clark, D. M. (2008). Why social anxiety persists: an experimental investigation of the role of safety behaviours as a maintaining factor. Journal of Behavior Therapy and Experimental Psychiatry, 39, 147161. https://doi.org/10.1016/j.jbtep.2006.12.002 CrossRefGoogle ScholarPubMed
Moorey, S. (2010). The six cycles maintenance model: growing a ‘vicious flower’ for depression. Behavioural and Cognitive Psychotherapy, 38, 173184. https://doi.org/10.1017/S1352465809990580 CrossRefGoogle ScholarPubMed
Muhammad, T., Meher, T., & Sekher, T. V. (2021). Association of elder abuse, crime victimhood and perceived neighbourhood safety with major depression among older adults in India: a cross-sectional study using data from the LASI baseline survey (2017–2018). BMJ Open, 11, e055625. https://doi.org/10.1136/bmjopen-2021-055625 CrossRefGoogle Scholar
Myin-Germeys, I., Kasanova, Z., Vaessen, T., Vachon, H., Kirtley, O., Viechtbauer, W., & Reininghaus, U. (2018). Experience sampling methodology in mental health research: new insights and technical developments. World Psychiatry, 17, 123132. https://doi.org/10.1002/wps.20513 CrossRefGoogle ScholarPubMed
NVivo version 12.0 (2020). QSR International Pty Ltd.Google Scholar
OECD (2024). Measuring subjective well-being across OECD countries: insights from the OECD Subjective Well-being Data Collection. OECD Publishing: https://doi.org/10.1787/5d6a83f3-en Google Scholar
Oren-Yagoda, R., Oren, B., & Aderka, I. M. (2024). Safety behaviors and positive emotions in social anxiety disorder. Journal of Anxiety Disorders. https://doi.org/10.1016/j.janxdis.2024.102915. Epub 2024 Aug 16.CrossRefGoogle ScholarPubMed
Paterson, C. (1996). Measuring outcomes in primary care: a patient generated measure, MYMOP, compared with the SF-36 healthy survey. BMJ, 312, 1016. https://doi.org/10.1136/bmj.312.7037.1016 CrossRefGoogle Scholar
Plummer, F., Manea, L., Trepel, D., & McMillan, D. (2016). Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. General Hospital Psychiatry, 39, 2431. https://doi.org/10.1016/j.genhosppsych.2015.11.005 CrossRefGoogle ScholarPubMed
Qin, N., & Yan, E. (2018). Common crime and domestic violence victimization of older Chinese in urban China: the prevalence and its impact on mental health and constrained behavior. Journal of Interpersonal Violence, 33, 889914. https://doi.org/10.1177/0886260517698825 CrossRefGoogle Scholar
R Core Team (2023). A language and environment for statistical computing. https://www.R-project.org/ Google Scholar
Rachman, S. (1997). A cognitive theory of obsessions. Behaviour Research and Therapy, 35, 793802.10.1016/S0005-7967(97)00040-5CrossRefGoogle ScholarPubMed
Rachman, S., Radomsky, A. S., & Shafran, R. (2008). Safety behaviour: a reconsideration. Behaviour Research and Therapy, 46, 163173. https://doi.org/10.1016/j.brat.2007.11.008 CrossRefGoogle ScholarPubMed
Regnault, A., Willgoss, T., Barbic, S., & International Society for Quality of Life Research Mixed Methods Special Interest Group (2017). Towards the use of mixed methods inquiry as best practice in health outcomes research. Journal of Patient Reported Outcomes, 2, 19. https://doi.org/10.1186/s41687-018-0043-8 CrossRefGoogle Scholar
Reisig, M. D., Holtfreter, K., & Turanovic, J. J. (2017). Criminal victimization, depressive symptoms, and behavioral avoidance coping in late adulthood: the conditioning role of strong familial ties. Journal of Adult Development, 25, 1324. https://doi.org/10.1007/s10804-017-9270-0 CrossRefGoogle Scholar
Roberto, K. A., & Hoyt, E. (2021). Abuse of older women in the United States: a review of empirical research, 2017–2019. Aggression and Violent Behavior, 57, 13591789. https://doi.org/10.1016/j.avb.2020.101487 CrossRefGoogle Scholar
Robitzsch, A. (2020). Why ordinal variables can (almost) always be treated as continuous variables: clarifying assumptions of robust continuous and ordinal factor analysis estimation methods. Frontiers in Education, 5, 17. https://doi.org/10.3389/feduc.2020.589965 CrossRefGoogle Scholar
Ruta, D. A., Garratt, A. M., Leng, M., Russell, I. T., & MacDonald, L. M. (1994). A new approach to the measurement of quality of life. The Patient-Generated Index. Medical Care, 32, 11091126. https://doi.org/10.1097/00005650-199411000-00004 CrossRefGoogle Scholar
Sales, C., Faísca, L., Ashworth, M, & Ayis, S. (2023). The psychometric properties of PSYCHLOPS, an individualized patient-reported outcome measure of personal distress. Journal of Clinical Psychology, 79, 622640.10.1002/jclp.23278CrossRefGoogle ScholarPubMed
Sales, C. M., Neves, I. T., Alves, P. G., & Ashworth, M. (2018). Capturing and missing the patient’s story through outcome measures: a thematic comparison of patient-generated items in PSYCHLOPS with CORE-OM and PHQ-9. Health Expectations, 21, 615619. https://doi.org/10.1111/hex.12652 CrossRefGoogle ScholarPubMed
Salkovskis, P. M. (1991). The importance of behaviour in the maintenance of anxiety and panic: a cognitive account. Behavioural Psychotherapy, 19, 619.10.1017/S0141347300011472CrossRefGoogle Scholar
Sanchez-Garcia, S., Garcia-Pena, C., Ramirez-Garcia, E., Moreno-Tamayo, K., & Cantu-Quintanilla, G. R. (2019). Decreased autonomy in community-dwelling older adults. Clinical Interventions in Aging, 14, 20412053. https://doi.org/10.2147/CIA.S225479 CrossRefGoogle ScholarPubMed
Satchell, J., Craston, T., Drennan, V. M., Billings, J., & Serfaty, M. (2022). Psychological distress and interventions for older victims of crime: a systematic review. Trauma, Violence, & Abuse, 24, 34933512. https://doi.org/10.1177/15248380221130354 CrossRefGoogle ScholarPubMed
Satchell, J., Dalrymple, N., Leavey, G., & Serfaty, M. (2023). ‘If we don’t forgive, it’s like holding on to them’: a qualitative study of religious and spiritual coping on psychological recovery in older crime victims. Psychological Trauma: Theory, Research, Practice, and Policy. Advance online publication, https://doi.org/10.1037/tra0001420 Google Scholar
Serfaty, M., Ridgewell, A., Drennan, V., Kessel, A., Brewin, C. R., Leavey, G., Wright, A., Laycock, G., & Blanchard, M. (2016). Helping aged victims of crime (the HAVoC study): common crime, older people and mental illness. Behavioural and Cognitive Psychotherapy, 44, 140155. https://doi.org/10.1017/S1352465814000514 CrossRefGoogle ScholarPubMed
Serfaty, M., Satchell, J., Lee, T., Laycock, G., Brewin, C., Buszewicz, M., Leavey, G., Drennan, V. M., Vickerstaff, V., Cooke, J., & Kessel, A. (2025). The VIP trial: a randomised controlled trial of the clinical effectiveness of a victim improvement package (VIP) for the reduction of continued symptoms of depression or anxiety in older victims of community crime in an English city. BMJ Open, 15, e095184. doi: 10.1136/bmjopen-2024-095184 CrossRefGoogle Scholar
Sharpe, L., Todd, J., Scott, A., Gatzounis, R., Menzies, R. E., & Meulders, A. (2022). Safety behaviours or safety precautions? The role of subtle avoidance in anxiety disorders in the context of chronic physical illness. Clinical Psychology Review, 92, 102126. https://doi.org/10.1016/j.cpr.2022.102126 CrossRefGoogle ScholarPubMed
Sherman, L., Neyroud, P. W., & Neyroud, E. (2016). The Cambridge Crime Harm Index: measuring total harm from crime based on sentencing guidelines. Policing, 10, 171183. https://doi.org/10.1093/police/paw003 CrossRefGoogle Scholar
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder. Archives of Internal Medicine, 166, 1092. https://doi.org/10.1001/archinte.166.10.1092 CrossRefGoogle ScholarPubMed
Staples, L. G., Dear, B. F., Gandy, M., Fogliati, V., Fogliati, R., Karin, E., Nielssen, O., & Titov, N. (2019). Psychometric properties and clinical utility of brief measures of depression, anxiety, and general distress: the PHQ-2, GAD-2, and K-6. General Hospital Psychiatry, 56, 1318. https://doi.org/10.1016/j.genhosppsych.2018.11.003 CrossRefGoogle ScholarPubMed
Telch, M. J., & Lancaster, C. L. (2012). Is there room for safety behaviors in exposure therapy for anxiety disorders? In Neudeck, P. & Wittchen, H.-U. (eds), Exposure Therapy (pp. 313334). https://doi.org/10.1007/978-1-4614-3342-2_18 CrossRefGoogle Scholar
Thornton, A., Hatton, C., Malone, C., Fryer, T., Walker, D., Cunningham, J., & Durrani, N. (2003). Distraction burglary among older adults and ethnic minority community. Home Office Research Study.Google Scholar
Thwaites, R., & Freeston, M. H. (2005). Safety-seeking behaviours: fact or function? How can we clinically differentiate between safety behaviours and adaptive coping strategies across anxiety disorders? Behavioural and Cognitive Psychotherapy, 33, 177188. https://doi.org/10.1017/s1352465804001985 CrossRefGoogle Scholar
Tripathi, K., Robertson, S., & Cooper, C. (2019). A brief report on older people’s experience of cybercrime victimization in Mumbai, India. Journal of Elder Abuse & Neglect, 31, 437447. https://doi.org/10.1080/08946566.2019.1674231 CrossRefGoogle ScholarPubMed
van der Willik, E., Terwee, C.B., Bos, W. J. W., Hemmelder, M. H., Jager, K. J., Zoccali, C., Dekker, F. W., Meuleman, Y. (2021). Patient-reported outcome measures (PROMS): making sense of individual PROM scores and changes in PROM scores over time. Nephrology, 26, 391399.10.1111/nep.13843CrossRefGoogle ScholarPubMed
Van Ruitenburg, T., & Ruiter, S. (2023). The adoption of a Crime Harm Index: a scoping literature review. Police Practice and Research, 24, 423445. https://doi.org/10.1080/15614263.2022.2125873 CrossRefGoogle Scholar
Werson, A. D., Meiser-Stedman, R., & Laidlaw, K. (2022). A meta-analysis of CBT efficacy for depression comparing adults and older adults. Journal of Affective Disorders, 319, 189201. https://doi.org/10.1016/j.jad.2022.09.020 CrossRefGoogle ScholarPubMed
Wild, B., Eckl, A., Herzog, W., Niehoff, D., Lechner, S., Maatouk, I., Schellberg, D., Brenner, H., Müller, H., & Löwe, B. (2014). Assessing generalized anxiety disorder in elderly people using the GAD-7 and GAD-2 scales: results of a validation Study. American Journal of Geriatric Psychiatry, 22, 10291038. https://doi.org/10.1016/j.jagp.2013.01.076 CrossRefGoogle ScholarPubMed
Willis, G. (1999). Cognitive Interviewing: A ‘How To’ Guide. Reducing Survey Error through Research on the Cognitive and Decision Processes in Surveys Short Course Presented at the 1999 Meeting of the American Statistical Association. Rachel A Caspar, Judith T. Lessler, and Gordon B. Willis--Research Triangle Institute.Google Scholar
World Health Organisation (2017). Fact sheet ‘mental health of older adults’. Retrieved 23 January 2022 from: http://www.who.int/news-room/fact-sheets/detail/mentalhealth-of-older-adults Google Scholar
Yunus, R. M., Hairi, N. N., & Choo, W. Y. (2019). Consequences of elder abuse and neglect: a systematic review of observational studies. Trauma, Violence & Abuse, 20, 197213. https://doi.org/10.1177/1524838017692798 CrossRefGoogle Scholar
Figure 0

Table 1. Sample characteristics (N=100)

Figure 1

Table 2. Proportion who endorsed each behaviour, and distribution on the Frequency and Change scales of those who endorsed (N=100)

Figure 2

Table 3. Summary overview of qualitative codes and themes in the PRSBM (N=100)

Figure 3

Table 4. Unique variable analysis

Figure 4

Table 5. Univariate logistic regression testing safety-seeking behaviours and psychological distress (n=100)

Figure 5

Table 6. Multi-variate logistic regression using backwards elimination (p<.05)

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