This study relied on the affective events theory and the social exchange theory to develop a framework that explains how situational factors (daily micro-interruptions) enhance affective reactions (negative affect) and, in turn, impair health conditions (mental health) at work. We further delineate theoretical arguments to propose the pet-human’s health effect by demonstrating that pets are boundary conditions that attenuate this relation, and as such are protective conditions for employees’ mental health. We conducted a 5-day diary study with two groups of participants, one with participants who owned pets (N = 82 x 5 = 410), and the other who did not own pets (N = 87 x 5 = 435). The multilevel results showed an indirect effect of daily micro-interruptions on individuals’ mental health through negative affect, with a daily backdrop of poorer mental health for those who did not own a pet (compared to those who owned a pet). These results evidence the benefits of owning a pet for individuals’ mental health, even at work, and as such provide recommendations for teleworking practices. Moreover, this study resorts to an innovative and robust data collection method to demonstrate the pet-human’ health effect. This study expands knowledge on the role of pets in working daily routines and shows that pets may be a personal resource for individuals while working.

Who joins extremist movements? Answering this question is beset by methodological challenges as survey techniques are infeasible and selective samples provide no counterfactual. Recruits can be assigned to contextual units, but this is vulnerable to problems of ecological inference. In this article, we elaborate a technique that combines survey and ecological approaches. The Bayesian hierarchical case–control design that we propose allows us to identify individual-level and contextual factors patterning the incidence of recruitment to extremism, while accounting for spatial autocorrelation, rare events, and contamination. We empirically validate our approach by matching a sample of Islamic State (ISIS) fighters from nine MENA countries with representative population surveys enumerated shortly before recruits joined the movement. High-status individuals in their early twenties with college education were more likely to join ISIS. There is more mixed evidence for relative deprivation. The accompanying extremeR package provides functionality for applied researchers to implement our approach.

Attempts to formalize inspection and monitoring strategies in industry have struggled to combine evidence from multiple sources (including subject matter expertise) in a mathematically coherent way. The perceived requirement for large amounts of data are often cited as the reason that quantitative risk-based inspection is incompatible with the sparse and imperfect information that is typically available to structural integrity engineers. Current industrial guidance is also limited in its methods of distinguishing quality of inspections, as this is typically based on simplified (qualitative) heuristics. In this paper, Bayesian multi-level (partial pooling) models are proposed as a flexible and transparent method of combining imperfect and incomplete information, to support decision-making regarding the integrity management of in-service structures. This work builds on the established theoretical framework for computing the expected value of information, by allowing for partial pooling between inspection measurements (or groups of measurements). This method is demonstrated for a simulated example of a structure with active corrosion in multiple locations, which acknowledges that the data will be associated with some precision, bias, and reliability. Quantifying the extent to which an inspection of one location can reduce uncertainty in damage models at remote locations has been shown to influence many aspects of the expected value of an inspection. These results are considered in the context of the current challenges in risk based structural integrity management.

The putative bilingual executive advantage has been argued to stem from lifelong experience with executively demanding language behaviors, such as switching between the two languages. However, studies testing for possible associations between language switching frequency and EF in bilinguals have yielded inconsistent results. One reason for this could lie in the methods used that have evaluated the frequency and type of language switches with retrospective self-reports, as well as in problems in reliability and convergent validity of the executive tasks. By using Ecological Momentary Assessment (EMA) as a reference point for self-reports of language switches, we examined the validity of general retrospective self-reports of language switching. Additionally, we examined associations between language switching and EF using multilevel models. Our results indicated that the commonly used retrospective self-reports of language switching may lack convergent validity. However, we found tentative evidence that contextual language switches, assessed with EMA, may be associated with better inhibitory control, set shifting, and working memory.

This study addresses why small parties nominate candidates to run in the district elections and how nomination of district candidates could influence small parties’ share of party votes in Taiwan. Previous studies on party's strategic entry in the mixed electoral system demonstrate the existence of ‘contamination effect’ in various Western democracies. While ‘contamination effect’ suggests that party would gain more proportional representation (PR) seats by increasing its number of candidate nomination in the single-member-district (SMD) races, we contend that small parties should also take the strength of nominated candidates into consideration. Nominating strong candidates in SMD competitions could generate positive ‘spillover effect’ to party's PR tier. By focusing on the 2016 Taiwan legislative election, our findings suggest that first, small parties need to fulfill the institutional requirements in order to qualify for running in the party-list election; second, the ‘contamination effect’ exists in Taiwan, but it is conditional; and finally, candidates’ strength creates positive ‘spillover effect’ on party's proportional seats.

Objectives: We sought to clarify the nature of self-reported cognitive function among healthy older adults by considering the short-term, within-person association (coupling) of subjective cognitive function with objective cognitive performance. We expected this within-person coupling to differ between persons as a function of self-perceived global cognitive decline and depression, anxiety, or neuroticism. Methods: This was an intensive measurement (short-term longitudinal) study of 29 older adult volunteers between the ages of 65 and 80 years without an existing diagnosis of dementia or mild cognitive impairment. Baseline assessment included neuropsychological testing and self-reported depression, anxiety, and neuroticism, as well as self- and informant-reported cognitive decline (relative to 10 years previously). Intensive within-person measurement occasions included subjective ratings of cognitive function paired with performance on a computerized working memory (n-back) task; each participant attended four or five assessments separated by intervals of at least one day. Statistical analysis was comprised of multilevel linear regression. Results: Comparison of models suggested that both neuroticism and self-rated cognitive decline explained unique variance in the within-person, across-occasion coupling of subjective cognitive function with objective working memory performance. Conclusions: Self-ratings of cognition may accurately reflect day-to-day variations in objective cognitive performance among older adults, especially for individuals lower in neuroticism and higher in self-reported cognitive decline. Clinicians should consider these individual differences when determining the validity of complaints about perceived cognitive declines in the context of otherwise healthy aging. (JINS, 2018, 24, 57–66)

Objectives: During a public health emergency, public health officials issue directives with actions people need to take to protect themselves. Past research has shown that adherence to these directives depends on individual beliefs and circumstances. This report presents new research about the effects of community factors on adherence.

Methods: A random digit-dial survey of 936 residents in the St Louis, Missouri, area was conducted in 2008 to assess barriers to and facilitators of adherence to directives issued in response to a hypothetical scenario involving the intentional release of the bacterium that causes plague. Community factors were assessed using characteristics of census tracts for individual respondents. Multilevel modeling was used to understand how individual and community factors contributed to the likelihood of adherence.

Results: The majority of participants indicated that they would adhere to 3 distinct directives. Community poverty and ethnic homogeneity as well as individual-level barriers were negatively associated with adherence to a 6-day quarantine. Having children younger than 18 years and being away from home when the directive was called were negatively associated with adherence to a 10-hour quarantine. Logistical concerns were negatively associated with visiting a point of dispensing for prophylactic antibiotics.

Conclusions: Our findings establish an empirical basis for the influence of community factors on adherence to public health directives. The influence of community and individual factors on adherence varies across directives. Consequently, communication strategies to disseminate directives and organizational strategies to support them must vary according to the nature of the directives.

(Disaster Med Public Health Preparedness. 2012;6:253–262)

Objectives: Learning effects may have considerable influence on the performance of new health technologies, thereby on cost-effectiveness and ultimately on resource allocation. In the area of radiotherapy, equipment is becoming increasingly costly and the analysis of learning effects is complex given that sequential treatments are necessary, with multiple sessions for each patient. Our study aimed at analyzing learning effects in radiotherapy at an early stage of innovation.

Methods: We used multilevel analysis to separate out the different learning effects of the new technique. Statistical analysis of observational data collected in a French National prospective survey was performed using an individual growth model. Intrapatient learning was modeled at level 1, and two types of interpatient learning were considered at level 2, regarding possible influences of professional experience on (i) the duration of each patient's first session in a given setting and (ii) the rate of change of session duration over time for a given patient. Conventional radiotherapy was also considered for comparison.

Results: Our results demonstrate a substantial type-1 interpatient learning effect and an even higher intrapatient learning effect. No type-2 interpatient learning was at work: professional experience did not impact intrapatient learning. Moreover, some intrapatient learning was also reported with conventional radiotherapy and was not significantly modified by innovation. Session duration was in any case strongly influenced by disease.

Conclusions: Because professionals highly underestimated the learning phenomenon, assessment of learning cannot be based on professional statements and it requires careful analysis of observational data.

Objectives: The impact of learning effects on the variability of costs of new health technologies in a prospective payment system (PPS) through the case of intensity modulated radiation therapy (IMRT) was studied.

Methods: A series of consecutive patients treated in nine medical centers was enrolled in a prospective study. Direct costs were assessed from the perspective of the healthcare providers. We used a two-level model to explain the variability of costs: patients nested within centers. Learning effects at the center level were considered through a fixed effect (the learning curve slope) and a random effect (the initial cost level). Covariates were introduced to explain the patterns of variation in terms of patient characteristics.

Results: The mean direct cost of IMRT was €5,962 (range, €2,414–€24,733). Manpower accounted for 53 percent of this cost. Learning effects explained 42 percent of the variance between centers (which was 88 percent of the total variance) and were associated with a substantial decrease in treatment costs. The mean initial treatment direct cost was €6,332 in centers with a previous experience of IMRT, whereas it was €14,192 in centers implementing IMRT for the first time. Including logistics costs and overhead, the full cost of IMRT was €10,916. Average reimbursement was €6,987.

Conclusions: Learning effects are a strong confounding factor in the analysis of costs of innovative health technologies involving learning effects. In a PPS, innovative health technology involving learning effects necessitates specific reimbursement mechanisms.