Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-10T09:46:43.621Z Has data issue: false hasContentIssue false
  • English
  • Français

A psychometric evaluation of the Family Decision-Making Self-Efficacy Scale among surrogate decision-makers of the critically ill

Published online by Cambridge University Press:  08 November 2019

Grant A. Pignatiello Open the ORCID record for Grant A. Pignatiello [Opens in a new window] ,
Elliane Irani ,
Sadia Tahir ,
Emily Tsivitse  and
Ronald L. Hickman Jr.
Grant A. Pignatiello*
Affiliation:
Case Western Reserve University, Cleveland, OH
Elliane Irani
Affiliation:
Case Western Reserve University, Cleveland, OH
Sadia Tahir
Affiliation:
Case Western Reserve University, Cleveland, OH
Emily Tsivitse
Affiliation:
Case Western Reserve University, Cleveland, OH
Ronald L. Hickman Jr.
Affiliation:
Case Western Reserve University, Cleveland, OH
*
Author for correspondence: Grant A. Pignatiello, 2120 Cornell Road, Cleveland, OH44106-4904, USA. E-mail: gap54@case.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives

The purpose of this study was to report the psychometric properties, in terms of validity and reliability, of the Unconscious Version of the Family Decision-Making Self-Efficacy Scale (FDMSE).

Methods

A convenience sample of 215 surrogate decision-makers for critically ill patients undergoing mechanical ventilation was recruited from four intensive care units at a tertiary hospital. Cross-sectional data were collected from participants between days 3 and 7 of a decisionally impaired patient's exposure to acute mechanical ventilation. Participants completed a self-report demographic form and subjective measures of family decision-making self-efficacy, preparation for decision-making, and decisional fatigue. Exploratory factor analyses, correlation coefficients, and internal consistency reliability estimates were computed to evaluate the FDMSE's validity and reliability in surrogate decision-makers of critically ill patients.

Results

The exploratory factor analyses revealed a two-factor, 11-item version of the FDMSE was the most parsimonious in this sample. Furthermore, modified 11-item FDMSE demonstrated discriminant validity with the measures of fatigue and preparation for decision-making and demonstrated acceptable internal consistency reliability estimates.

Significance of results

This is the first known study to provide evidence for a two-factor structure for a modified, 11-item FDMSE. These dimensions represent treatment and palliation-related domains of family decision-making self-efficacy. The modified FDMSE is a valid and reliable instrument that can be used to measure family decision-making self-efficacy among surrogate decision-makers of the critically ill.

Keywords

Critical careDecision-makingFamily membersPsychometricsSelf-efficacy
Type
Original Article
Information
Palliative & Supportive Care , Volume 18 , Issue 5 , October 2020 , pp. 537 - 543
Check for updates
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aslakson, RA, Curtis, JR and Nelson, JE (2014) The changing role of palliative care in the ICU. Critical Care Medicine 42(11), 2418–2428. doi:10.1097/CCM.0000000000000573CrossRefGoogle ScholarPubMed
Bandura, A. (1994). Self-efficacy. In Ramachaudran, VS (ed.), Encyclopedia of Human Behavior, 4(4). New York, NY: Academic Press, pp. 71–81.Google Scholar
Bandura, A (2006) Guide for constructing self-efficacy scales. Self-Efficacy Beliefs of Adolescents 5(1), 307–337.Google Scholar
Bennett, C, Graham, ID, Kristjansson, E, et al. (2010) Validation of a preparation for decision making scale. Patient Education and Counseling 78(1), 130–133. doi:10.1016/j.pec.2009.05.012CrossRefGoogle ScholarPubMed
Cattell, RB (1966) The scree test for the number of factors. Multivariate Behavioral Research 1(2), 245–276.CrossRefGoogle ScholarPubMed
Costello, AB and Osborne, JW (2005) Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation 10(7), 1–9.Google Scholar
Critical Care Statistics (2019). Available at: https://www.sccm.org/Communications/Critical-Care-Statistics (Retrieved from July 1, 2019).Google Scholar
Curran, PJ, West, SG and Finch, JF (1996) The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods 1(1), 16.CrossRefGoogle Scholar
Daly, BJ, Douglas, SL, O'Toole, E, et al. (2018) Complexity analysis of decision-making in the critically ill. Journal of Intensive Care Medicine 33(10), 557–566. doi:10.1177/0885066616678394CrossRefGoogle ScholarPubMed
Dionne-Odom, JN, Ejem, D, Azuero, A, et al. (2018) Factors associated with family caregivers’ confidence in future surrogate decision making for persons with cancer. Journal of Palliative Medicine. doi:10.1089/jpm.2018.0148CrossRefGoogle ScholarPubMed
Duckworth, KE, Forti, AM, Russell, GB, et al. (2014) The process of advance care planning in HCT candidates and proxies: Self-efficacy, locus of control, and anxiety levels. American Journal of Hospice and Palliative Care 31(7), 710–716. doi:10.1177/1049909113499815CrossRefGoogle ScholarPubMed
Fried, TR, Zenoni, M, Iannone, L, et al. (2019) Assessment of surrogates’ knowledge of patients’ treatment goals and confidence in their ability to make surrogate treatment decisions. JAMA Internal Medicine 179(2), 267–268. doi:10.1001/jamainternmed.2018.5299CrossRefGoogle ScholarPubMed
Gillespie, DF and Perron, BE (2015) Key Concepts in Measurement. Oxford University Press.Google Scholar
Green, MJ, Van Scoy, LJ, Foy, AJ, et al. (2018) A randomized controlled trial of strategies to improve family members’ preparedness for surrogate decision-making. American Journal of Hospice and Palliative Medicine 35(6), 866–874.CrossRefGoogle ScholarPubMed
Gullo, MJ, Dawe, S, Kambouropoulos, N, et al. (2010) Alcohol expectancies and drinking refusal self-efficacy mediate the association of impulsivity with alcohol misuse. Alcoholism: Clinical and Experimental Research 34(8), 1386–1399.Google ScholarPubMed
Harris, PA, Taylor, R, Thielke, R, et al. (2009) Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics 42(2), 377–381.CrossRefGoogle ScholarPubMed
Hickman, RL Jr, Pignatiello, GA and Tahir, S (2018) Evaluation of the decisional fatigue scale among surrogate decision makers of the critically ill. Western Journal of Nursing Research 40(2), 191–208.CrossRefGoogle ScholarPubMed
Hoffer, LJ and Bistrian, BR (2016) Nutrition in critical illness: A current conundrum. F1000Research 5, 2531–2531. doi:10.12688/f1000research.9278.1CrossRefGoogle ScholarPubMed
Johnson, RL and Morgan, GB (2016). Survey Scales: A Guide to Development, Analysis, and Reporting. New York, NY: Guilford Publications.Google Scholar
Kon, AA, Davidson, JE, Morrison, W, et al. (2016) Shared decision making in intensive care units: An American College of Critical Care Medicine and American Thoracic Society Policy Statement. Critical Care Medicine 44(1), 188–201. doi:10.1097/ccm.0000000000001396CrossRefGoogle ScholarPubMed
Kruser, JM, Cox, CE and Schwarze, ML (2017) Clinical momentum in the intensive care unit. A latent contributor to unwanted care. Annals of the American Thoracic Society 14(3), 426–431. doi:10.1513/AnnalsATS.201611-931OICrossRefGoogle ScholarPubMed
Mack, JW, Weeks, JC, Wright, AA, et al. (2010) End-of-life discussions, goal attainment, and distress at the end of life: Predictors and outcomes of receipt of care consistent with preferences. Journal of Clinical Oncology 28(7), 1203.CrossRefGoogle ScholarPubMed
Majesko, A, Hong, SY, Weissfeld, L, et al. (2012) Identifying family members who may struggle in the role of surrogate decision maker. Critical Care Medicine 40(8), 2281–2286. doi:10.1097/CCM.0b013e3182533317CrossRefGoogle ScholarPubMed
Miller, SM, Hudson, SV, Egleston, BL, et al. (2013) The relationships among knowledge, self-efficacy, preparedness, decisional conflict, and decisions to participate in a cancer clinical trial. Psycho-oncology 22(3), 481–489. doi:10.1002/pon.3043CrossRefGoogle Scholar
Multon, KD, Brown, SD and Lent, RW (1991) Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology 38(1), 30–38. doi:10.1037/0022-0167.38.1.30CrossRefGoogle Scholar
Netzer, G and Sullivan, DR (2014) Recognizing, naming, and measuring a family intensive care unit syndrome. Annals of the American Thoracic Society 11(3), 435–441.CrossRefGoogle ScholarPubMed
Nolan, MT, Hughes, MT, Kub, J, et al. (2009) Development and validation of the Family Decision-Making Self-Efficacy Scale. Palliative & Supportive Care 7(3), 315–321. doi:10.1017/S1478951509990241CrossRefGoogle ScholarPubMed
O'Connor, AM, Tugwell, P, Wells, GA, et al. (1998) A decision aid for women considering hormone therapy after menopause: Decision support framework and evaluation. Patient Education and Counseling 33(3), 267–279.CrossRefGoogle ScholarPubMed
Pett, MA, Lackey, NR and Sullivan, JJ (2003) Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
Pignatiello, GA, Martin, RJ and Hickman, RL Jr (2018) Decision fatigue: A conceptual analysis. Journal of Health Psychology. doi:10.1177/1359105318763510.Google ScholarPubMed
Pisani, MA, McNicoll, L and Inouye, SK (2003) Cognitive impairment in the intensive care unit. Clinics in Chest Medicine 24(4), 727–737.CrossRefGoogle ScholarPubMed
Prince, M, Ali, G-C, Guerchet, M, et al. (2016) Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimer's Research & Therapy 8(1), 23–23. doi:10.1186/s13195-016-0188-8CrossRefGoogle ScholarPubMed
Raghupathi, W and Raghupathi, V (2018) An empirical study of chronic diseases in the United States: A visual analytics approach. International Journal of Environmental Research and Public Health 15(3), 431. doi:10.3390/ijerph15030431CrossRefGoogle ScholarPubMed
Shultz, KS, Whitney, DJ and Zickar, MJ (2013) Measurement Theory in Action: Case Studies and Exercises. New York, NY: Routledge.CrossRefGoogle Scholar
Song, M-K, Ward, SE and Lin, F-C (2012) End-of-life decision-making confidence in surrogates of African-American dialysis patients is overly optimistic. Journal of Palliative Medicine 15(4), 412–417. doi:10.1089/jpm.2011.0330CrossRefGoogle ScholarPubMed
Stajkovic, AD and Luthans, F (1998) Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin 124(2), 240.CrossRefGoogle Scholar
Strecher, VJ, McEvoy DeVellis, B, Becker, MH, et al. (1986) The role of self-efficacy in achieving health behavior change. Health Education Quarterly 13(1), 73–92.CrossRefGoogle ScholarPubMed
Sulmasy, DP, Hughes, MT, Yenokyan, G, et al. (2017) The trial of ascertaining individual preferences for loved ones’ role in end-of-life decisions (TAILORED) study: A randomized controlled trial to improve surrogate decision making. Journal of Pain and Symptom Management 54(4), 455–465. doi:10.1016/j.jpainsymman.2017.07.004CrossRefGoogle ScholarPubMed
Taber, KS (2018) The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education 48(6), 1273–1296. doi:10.1007/s11165-016-9602-2CrossRefGoogle Scholar
Thompson, CJ, Bridier, N, Leonard, L, et al. (2018) Exploring stress, coping, and decision-making considerations of Alzheimer's family caregivers. Dementia (London). 1471301218809865. doi:10.1177/1471301218809865Google ScholarPubMed
Vancouver, JB, Thompson, CM, Tischner, EC, et al. (2002) Two studies examining the negative effect of self-efficacy on performance. The Journal of Applied Psychology 87(3), 506–516.CrossRefGoogle ScholarPubMed
Wendler, D and Rid, A (2011) Systematic review: The effect on surrogates of making treatment decisions for others. Annals of Internal Medicine 154(5), 336–346. doi:10.7326/0003-4819-154-5-201103010-00008CrossRefGoogle ScholarPubMed
White, DB (2011) Rethinking interventions to improve surrogate decision making in intensive care units. American Journal of Critical Care 20(3), 252–257.CrossRefGoogle ScholarPubMed
White, DB, Angus, DC, Shields, A-M, et al. (2018) A randomized trial of a family-support intervention in intensive care units. New England Journal of Medicine 378(25), 2365–2375. doi:10.1056/NEJMoa1802637CrossRefGoogle ScholarPubMed
Williams, B, Onsman, A and Brown, T (2010) Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine 8, 3.CrossRefGoogle Scholar
Zier, LS, Sottile, PD, Hong, SY, et al. (2012) Surrogate decision makers’ interpretation of prognostic information: A mixed-methods study. Annals of Internal Medicine 156(5), 360–366. doi:10.7326/0003-4819-156-5-201203060-00008CrossRefGoogle ScholarPubMed