Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: J Police Crim Psychol. 2021 Sep 4;37(1):141–145. doi: 10.1007/s11896-021-09471-w

A Confirmatory Factor Analysis of the PROMIS Sleep Disturbance Among Law Enforcement Officers

Kaylie Green 1, Ashley Eddy 1, Jenna Flowers 1, Michael Christopher 1
PMCID: PMC9373046  NIHMSID: NIHMS1825103  PMID: 35966282

Abstract

Law enforcement officers (LEOs) are at increased risk for sleep disorders relative to the general population. Common LEO occupational stressors, including critical incidents and shift work, predict sleep disturbance, which in turn negatively impacts health, performance, and community safety. The Patient-Reported Outcomes Measurement Information System-Sleep Disturbance 4-item (PROMIS SD4) was developed to assess self-reported sleep quality, satisfaction, and difficulties falling asleep. Previous studies suggest PROMIS-SD short-forms (4-, 6-, and 8-item) have good psychometric properties; however, evaluation of this easily-administered measure in high-stress, frontline populations is limited. The primary goal of this study was to evaluate the validity and reliability of the PROMIS-SD4 in a sample of LEOs (N = 111). A confirmatory factor analysis suggests that the original one-factor solution, with a correlated error-term, provides an excellent fit to the data, SBχ2(2) = 1.62, p = .23, CFI = .99, RMSEA = .12, SRMR = .01. The PROMIS SD4 demonstrated good reliability (α = .85) and evidence of convergent validity correlations in the expected direction with domains of psychological distress, positive health outcomes, reactivity, and body experience (all p’s < .05). Results suggest that the PROMIS-SD4 is a valid and reliable measure of sleep disturbance among LEOs.

Keywords: Law enforcement, psychometrics, sleep quality, first responder, occupational health

Law enforcement officers (LEOs) experience high levels of stress stemming from regular exposure to critical incidents and other occupation-related stressors (Violanti et al. 2016). Acute and chronic LEO occupational stressors are directly linked to a number of negative mental health outcomes, including sleep disturbance (Hartley et al. 2014). In terms of prevalence rates, 10–25% of the United States adult population meet diagnostic criteria for a sleep disorder (Ohayon 2011), whereas rates are higher for LEOs at approximately 40% (Garbarino et al. 2019; Rajaratnam et al. 2011). Additional occupational demands, such as shift work and regular overtime, make it difficult for LEOs to maintain healthy sleeping patterns, and contribute to poor sleep quality (Ma et al. 2015; Rajaratnam et al. 2011).

Sleep disturbances negatively impact cognitive and emotional functioning, both of which are essential to the high-risk, fast-paced nature of policing. More specifically, poor sleep quality leads to deficits in sustained attention, vigilant awareness, learning, content memorization, and emotion regulation (Anderson & Platten 2011), as well as irritability and increased impulsivity to negative stimuli (Deliens et al. 2014). In LEO samples, sleep disturbance also increases the perception of stress (Ma et al. 2019) and the risk for developing anxiety, depression, and PTSD (Neylan 2013). The risks associated with sleep deprivation among LEOs can also impact the public. Sleep deprived LEOs are more likely to make mistakes that jeopardize public safety, including increased implicitly biased associations (James 2017), anticipatory errors (Bell et al. 2015), and falling asleep while driving (Rajaratnam et al. 2011). Brief, accessible measures for use among LEOs is vital to detection of sleep disturbances, and ultimately to prevention and treatment.

The National Institutes of Health Patient-Reported Outcomes Measurement Information System – Sleep Disturbance (PROMIS SD) assesses perceptions of sleep quality (Cella et al. 2010). The PROMIS SD 4-item version (PROMIS SD4) is a short-form of the PROMIS SD 27-item bank. The original PROMIS SD was developed by sleep disturbance experts using an iterative process of stakeholder interviews, focus groups, cognitive interviewing, and classical test theory and item response theory (Cella et al. 2010). The development of the PROMIS SD addresses criticisms of other sleep disturbance measures, including outdated validation methods and non-generalizable items (Yu et al. 2011). The PROMIS SD4 has been validated in the general population (Yu et al. 2011), adolescents (Hanish et al. 2017), ethnically diverse adult cancer patients (Jensen et al. 2016), and caregivers (Carlozzi et al. 2019); however, we are unaware of any studies that have examined its psychometric characteristics in a LEO sample. Given that LEOs experience unique stressors and high rates of sleep disorders, the goal of this study was to assess the validity and reliability of the PROMIS SD4 among LEOs.

Method

Participants

LEOs were recruited from the Pacific Northwestern United States. Baseline data were combined from two separate clinical trials: study 1 (n = 60; see Christopher et al. 2016 for study details) and Study 2 (n = 61; see Christopher et al. 2018 for study details) for a total N = 121. Separate and total sample demographics are presented in Table 1.

Table 1.

Participant demographics.

N = 121
Mean (SD)
Age 43.1 (7.0)
Years of Education ---
Years on the force 15.8 (6.4)
Percentage (%)
Gender (%)
 Male 76.0%
 Female 24.0%
Race (%)
 White 87.6%
 Black 5.0%
 Native Hawaiian/Pacific Islander 1.7%
 Native American/Alaskan 0.8%
 Asian 2.4%
 Multiracial 0.8%
 Other 1.7%
Ethnicity (%)
 Hispanic/Latinx 8.2%
 Not Hispanic/Latinx 90.1%
Open in a new tab

Procedures and Measures

The Pacific University IRB approved all procedures in study 1 and 2. After providing written informed consent, participants completed all self-report validated measures below online via Qualtrics (Provo, UT). Unless otherwise noted, the following validated self-report measures were used in studies 1 and 2:

PROMIS Short-Form Measures.

Sleep Disturbance (4-item), Anger (5-item), Pain Interference (4-item) [study 1 only], Global Physical and Mental Health (8-item) [study 1 only], Fatigue (4-item) [study 1 only], Anxiety (6-item) [study 2 only], and Depression (6-item) [study 2 only] (Cella et al. 2010).

Additional measures.

Dispositional Mindfulness (Five Facet Mindfulness Questionnaire – Short Form [FFMQ-SF; Bohlmeijer et al. 2011]; Suicidal Ideation (Concise Health Risk Tracking Self-Report Scale [CHRT-SR; Trivedi, et al., 2011, study 2 only]); Experience of Occupational Stress (Police Stress Questionnaire [PSQ; McCreary & Thompson 2006]); Burnout (The Oldenburg Burnout Inventory [OLBI; Halbesleben & Demerouti 2005]); and Wellbeing (World Health Organization Well-Being Index [WHO-5; World Health Organization 1998, study 2 only]).

Statistical Analysis

To assess factorial validity, an item-level PROMIS SD4 confirmatory factor analysis (CFA) model was tested using robust diagonal weighted least squares (RDWLS) estimation with LISREL 10.20 (Jöreskog & Sörbom 2019). RDWLS was used due to the ordinal scale nature of the PROMIS SD4 (i.e., Likert-type items) and related issues of multivariate non-normality. We used a procedure described by Jöreskog (2004) in which polychoric correlations are estimated and then rescaled into a polychoric covariance matrix. To assess the fit of the data to the model, four fit indices were evaluated: the Satorra Bentler adjusted chi-square χ2 (SBχ2; Satorra & Bentler 2001), the comparative fit index (CFI; Bentler 1990), the root mean square error of approximation (RMSEA; Marsh, Balla & Hau 1996), and the standardized root mean squared residual (SRMR; Hu & Bentler 1999). RMSEA values of .06 or less are thought to indicate a close fit, .08 a fair fit, and .10 a marginal fit, CFI values of .95 and greater, and SRMR values of approximately .09 or less tend to indicate good fit (Hu & Bentler 1999). Cronbach’s alpha was used to assess internal consistency and Pearson’s correlations to assess convergent validity.

Results

The specified CFA model consisted of four observed variables (i.e., the four PROMIS SD4 items). The fit of the model was as follows: SBχ2(2) = 11.20, p = .003; CFI = .97, RMSEA = .26 (90% confidence interval = .16-.38), SRMR = .04. Overall, the item-level PROMIS SD4 model provided an acceptable fit to the data, with 2 of 4 fit indices suggesting a good model fit. All items significantly loaded onto the latent factor (item 1 = .94, item 2 = .90, item 3 = .87, item 4 = .58). Modification indices suggested that the errors between items 3 and 4 were correlated. Given the conceptual overlap (item 3: [I had a problem with my sleep...] and item 4: I had difficulty falling asleep]), but unique variance accounted for by each item (ritem3/item4 = .55), we ran a post hoc model allowing item 3 and 4 error terms to covary. Adding the error covariance significantly improved model fit: SBχ2(1) = 1.62, p = .23; CFI = .99, RMSEA = .12 (90% confidence interval = .01-.31), SRMR = .01; ΔSBχ2(1) = 9.58, df = 1, p < .001; ΔCFI = .02). The PROMIS SD4 also demonstrated good reliability (α = .85), and as shown in Table 2, PROMIS SD4 was significantly correlated in the expected direction with measures of psychological distress, wellbeing, reactivity, and body experience (all p’s < .05).

Table 2.

Correlation between PROMIS sleep disturbance and conceptually related experiences.

Psychological Distress
PROMIS – ANX a PROMIS – D a PROMIS – MH b CHRT – SR a
.245*** .417** −.383** .285**
Wellbeing
FFMQ c WHO – 5 a
−.237* −.513*
Reactivity
PROMIS – ANG c PSQ-ORG c PSQ – OP c OLBI c
.279* .297* .410* .221**
Body Experience
PROMIS – Pain b PROMIS – F b PROMIS – PH b
.297** .500* −.420*
Open in a new tab

Note.

*

p < .01.

**

p < .05.

***

p < .10

a

n = 61

b

n = 60

c

N = 121

Discussion

The goal of this study was to assess the validity and reliability of the PROMIS SD4 among LEOs. Results suggest the PROMIS SD4 has evidence of factorial and convergent validity, and well as internal consistency among LEOs. These results are consistent with PROMIS SD4 validation studies in the general population (Yu et al. 2011), adolescents (Hanish et al. 2017), ethnically diverse adult cancer patients (Jensen et al. 2016), and caregivers (Carlozzi et al. 2019).

Overall, the 4-item CFA provided a good fit to the data. Although the RMSEA value (.12) was indicative of poor fit, RMSEA is often artificially inflated in models with low degrees of freedom (Kenny, Kaniskan, & McCoach 2015). In addition, correlated error rates between items 3 [I had a problem with my sleep…] and 4 [I had difficulty falling asleep...], suggested conceptual overlap; however, these items contributed unique variance supporting inclusion of both. Overall, each item loaded on the latent construct demonstrating PROMIS SD4 validity in assessing sleep disturbance characteristics of sleep quality, sleep satisfaction, and difficulties falling asleep.

The PROMIS SD4 was correlated in the expected direction (all p’s > .05) with all measures of psychological distress (anxiety, depression, global mental health, suicidal ideation), wellbeing (mindfulness, wellbeing), reactivity (anger, burnout, experience of occupational stress) and body experience (pain, fatigue, global physical health). These results are similar to previous LEO studies in which other measures of sleep disturbance correlated with measures of psychological distress (Everding et al. 2016), wellbeing (Hartley et al. 2015), reactivity (Everding et al. 2016; Rajaratnam et al. 2011), and body experience (Everding et al. 2016; Rajaratnam et al. 2011). Results of this study demonstrated factorial and convergent validity, as well as internal consistency among LEOs, providing evidence that the PROMIS SD4 is a valid and reliable measure to use among LEO populations.

Findings from the current study support PROMIS SD4 use among LEOs in clinical, research, and non-clinical settings. LEOs experience unique occupational stressors leading to high rates of sleep disorders. It is important to have a brief, valid measure of sleep disturbance for this high-stress population where sleep is crucial at both the individual and community level. The reliability and validity of the PROMIS SD4 support its inclusion as a brief measure in research settings to further understand how sleep impacts high stress populations. This finding is consistent with previous studies that have supported the inclusion of PROMIS SD short-forms as brief measures in longer assessment batteries (Carlozzi et al. 2019; Yu et al. 2012). Additionally, the PROMIS SD4 appears to be a valid and reliable measure for use in clinical assessment settings to understand individual difficulties with sleep for LEOs, screen for potential sleep disturbances (which may warrant further evaluation), and to monitor treatment progress. This finding is consistent with several previous studies which advocate the use of PROMIS SD short-forms in clinical populations (e.g., Jensen et al. 2016). The PROMIS SD4 may also be used by non-clinicians for regular sleep difficulty check-ups as part of department wellness initiatives.

There are a number of limitations to this study. First, the sample was from a relatively small, yet concentrated geographical location, which may impact the generalizability of the findings. Second, relatedly, this was a largely homogeneous sample (primarily white males), which further precludes generalizability of findings. Future studies with larger and more diverse LEO samples are needed. Third, there is an inherent limitation when utilizing self-report measures to assess sleep quality (Matthews et al. 2018). It would be advantageous for future studies to compare responses on the PROMIS SD4 to objective measures. Lastly, data from this study was assessed using a cross-sectional design. Additional research is needed to analyze the factor structure of the PROMIS SD4 in longitudinal clinical trials.

Overall, the results from this study support our hypothesis that the PROMIS SD4 is a psychometrically sound measure to assess perceptions of sleep quality among LEOs. This finding provides clinicians and researchers a useful tool to further investigate important sleep implications among LEOs. Populations such as firefighters, paramedics, and nurses may also benefit from additional sleep research using a brief, valid, and reliable measure.

Funding

Research reported in this publication was supported by the National Center for Complementary & Integrative Health of the National Institutes of Health under Award Number R21AT008854. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflicts of interest/Competing interests

Dr. Christopher received funding from the National Institutes of Health during the conduct of the study. Ms. Green, Ms. Flowers, and Ms. Eddy have no funding to disclose. My co-authors and I do not have any interests that might be interpreted as influencing this research

Declarations

Ethics approval

The Pacific University IRB approved all procedures (IRB# 089–15)

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

All authors provided consent to submit this manuscript for publication.

Code availability

NA

Trial registration number: ClinicalTrials.gov Identifier: NCT02521454

Availability of data and material

The datasets in the current study are available from the corresponding author on reasonable request.

References

  1. Anderson C, & Platten CR (2011). Sleep deprivation lowers inhibition and enhances impulsivity to negative stimuli. Behavioural Brain Research, 217(2), 463–466. 10.1016/j.bbr.2010.09.020 [DOI] [PubMed] [Google Scholar]
  2. Baer RA, Smith GT, Hopkins J, Krietemeyer J, & Toney L (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13(1), 27–45. doi: 10.1177/1073191105283504 [DOI] [PubMed] [Google Scholar]
  3. Bell LB, Virden TB, Lewis DJ, & Cassidy BA (2015). Effects of 13-hour 20-minute work shifts on law enforcement officer’s sleep, cognitive abilities, health, quality of life, and work performance. Police Quarterly, 18(3), 293–337. doi: 10.1177/1098611115584910 [DOI] [Google Scholar]
  4. Bentler PM (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246. [DOI] [PubMed] [Google Scholar]
  5. Bohlmeijer E, Ten Klooster PM, Fledderus M, Veehof M, & Baer R (2011). Psychometric properties of the Five Facet Mindfulness Questionnaire in depressed adults and development of a short form. Assessment, 18(3), 308–320. doi: 10.1177/1073191111408231 [DOI] [PubMed] [Google Scholar]
  6. Carlozzi NE, Ianni PA, Tulsky DS, Brickell TA, Lange RT, French LM, Cella D, Kallen MA, Miner JA, Kratz AL (2019). Understanding health-related quality of life in caregivers of civilians and service members/veterans with traumatic brain injury: Establishing the reliability and validity of PROMIS Fatigue and Sleep Disturbance item banks. Archives of Physical Medicine and Rehabilitation, 100(4), S102–S109. doi: 10.1016/j.apmr.2018.05.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, Hays R (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. 10.1016/j.jclinepi.2010.04.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Charles LE, Slaven JE, Mnatsakanova A, Ma C, Violanti JM, Fekedulegn D, Andrew ME, Vila BJ, Burchfiel CM (2011). Association of perceived stress with sleep duration and sleep quality in police officers. International Journal of Emergency Mental Health, 13(4), 229. [PMC free article] [PubMed] [Google Scholar]
  9. Christopher MS, Goerling RJ, Rogers BS, Hunsinger M, Baron G, Bergman AL, Zava DT, 2016. A pilot study evaluating the effectiveness of a mindfulness-based intervention on cortisol awakening response and health outcomes among law enforcement officers. Journal of Police and Criminal Psychology, 31(1), 15–28. [Google Scholar]
  10. Christopher MS, Hunsinger M, Goerling R, Bowen S, Rogers B, Gross CR, Dapolonia E, & Pruessner J (2018). Mindfulness-Based Resilience Training to reduce health risk, stress reactivity, and aggression among law enforcement officers: A feasibility and preliminary efficacy trial. Psychiatry Research, 264, 104–115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Deliens G, Gilson M, & Peigneux P (2014). Sleep and the processing of emotions. Experimental Brain Research, 232(5), 1403–1414. 10.1007/s00221-014-3832-1 [DOI] [PubMed] [Google Scholar]
  12. Everding B, Hallam JE, Kohut ML, Lee DC., Anderson AA, & Franke WD (2016). Association of sleep quality with cardiovascular disease risk and mental health in law enforcement officers. Journal of Occupational and Environmental Medicine, 58(8), e281–e286. doi: 10.1097/jom.0000000000000814 [DOI] [PubMed] [Google Scholar]
  13. Garbarino S, Guglielmi O, Puntoni M, Bragazzi NL, & Magnavita N (2019). Sleep quality among police officers: implications and insights from a systematic review and meta-analysis of the literature. International Journal of Environmental Research and Public Health, 16(5), 885. 10.3390/ijerph16050885 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Halbesleben JR, & Demerouti E (2005). The construct validity of an alternative measure of burnout: Investigating the English translation of the Oldenburg Burnout Inventory. Work & Stress, 19(3), 208–220. 10.1080/02678370500340728 [DOI] [Google Scholar]
  15. Hanish AE, Lin-Dyken DC, & Han JC (2017). PROMIS sleep disturbance and sleep-related impairment in adolescents: Examining psychometrics using self-report and actigraphy. Nursing Research, 66(3), 246. doi: 10.1097/NNR.0000000000000217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hartley TA, Violanti JM, Sarkasian K, Fekedulegn D, Mnatsaksnova A, Andrew ME, & Burchfiel CM (2014). Association between police-specific stressors and sleep quality: Influence of coping and depressive symptoms. Journal of Law Enforcement and Leadership Ethics, 1, 31–48. [PMC free article] [PubMed] [Google Scholar]
  17. Hu L, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi: 10.1080/10705519909540118 [DOI] [Google Scholar]
  18. James L (2017). The stability of implicit racial bias in police officers. Police Quarterly, 21(1), 30–52. doi: 10.1177/1098611117732974 [DOI] [Google Scholar]
  19. Jensen RE, King-Kallimanis BL, Sexton E, Reeve BB, Moinpour CM, Potosky AL, Lobo T, & Teresi JA (2016). Measurement properties of PROMIS sleep disturbance short forms in a large, ethnically diverse cancer cohort. Psychological Test and Assessment Modeling, 58(2), 353. [Google Scholar]
  20. Jöreskog KG, & Sörbom D (2019). LISREL 10.20. Chicago: Scientific Software International. [Google Scholar]
  21. Jöreskog KG (2004). Structural equation modeling with ordinal variables using LISREL. Retrieved from http://www.ssicentral.com/lisrel/techdocs/ordinal.pdf
  22. Kenny DA, Kaniskan B, & McCoach DB (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507. 10.1177/0049124114543236 [DOI] [Google Scholar]
  23. Ma CC, Andrew ME, Fekedulegn D, Gu JK, Hartley TA, Charles LE, Violanti JM, Burchfiel CM (2015). Shift work and occupational stress in police officers. Safety and Health at Work, 6(1), 25–29. 10.1016/j.shaw.2014.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Matthews KA, Patel SR, Pantesco EJ, Buysse DJ, Kamarck TW, Lee L, & Hall MH (2018). Similarities and differences in estimates of sleep duration by polysomnography, actigraphy, diary, and self-reported habitual sleep in a community sample. Sleep health, 4(1), 96–103. doi: 10.1016/j.sleh.2017.10.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Marsh HW, Balla JR, & Hau KY (1996). An evaluation of incremental fit indices: A clarification of mathematical and empirical properties. In Marcoulides GA & Schumaker RE (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 315–353). Mahwah, NJ: Lawrence Erlbaum. [Google Scholar]
  26. McCreary DR, & Thompson MM (2006). Development of two reliable and valid measures of stressors in policing: the operational and organizational police stress questionnaires. International Journal of Stress Management, 13(4), 494–518. 10.1037/1072-5245.13.4.494 [DOI] [Google Scholar]
  27. McCreary DR, Fong I, & Groll DL (2017). Measuring policing stress meaningfully: establishing norms and cut-off values for the Operational and Organizational Police Stress Questionnaires. Police Practice and Research, 18(6), 612–623. 10.1080/15614263.2017.1363965 [DOI] [Google Scholar]
  28. Neylan TC (2013). Sleep and psychological vulnerability to traumatic stress. Sleep, 36(7), 967–968. doi: 10.5665/sleep.2782 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ohayon MM (2011). Epidemiological overview of sleep disorders in the general population. Sleep Medicine Research, 2(1), 1–9. doi: 10.17241/smr.2011.2.1.1 [DOI] [Google Scholar]
  30. Ong AD, Kim S, Young S, & Steptoe A (2017). Positive affect and sleep: A systematic review. Sleep Medicine Reviews, 35, 21–32. 10.1016/j.smrv.2016.07.006 [DOI] [PubMed] [Google Scholar]
  31. Prather AA, Bogdan R, & Hariri PAR (2013). Impact of sleep quality on amygdala reactivity, negative affect, and perceived stress. Psychosomatic Medicine, 75(4), 350. doi: 10.1097/PSY.0b013e31828ef15b [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Rajaratnam SMW, Barger LK, Lockley SW, Shea SA, Wang W, Landrigan CP, O’Brien CP, Quadri S, Sullivan JP, Cade BE, Epstein LJ, Czeisler CA (2011). Sleep disorders, health, and safety in police officers. JAMA, 306(23), 2567. doi: 10.1001/jama.2011.1851 [DOI] [PubMed] [Google Scholar]
  33. Satorra A, & Bentler PM (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Smith MT, Huang MI, & Manber R (2005). Cognitive behavior therapy for chronic insomnia occurring within the context of medical and psychiatric disorders. Clinical Psychology Review, 25(5), 559–592. 10.1016/j.cpr.2005.04.004 [DOI] [PubMed] [Google Scholar]
  35. Trivedi MH, Wisniewski SR, Morris DW, Fava M, Gollan JK, Warden D, Nierenberg AA, Gaynes BN, Husain MM, Luther JF, & Zisook S (2011). Concise Health Risk Tracking scale: A brief self-report and clinician rating of suicidal risk. Journal of Clinical Psychiatry, 72(6), 757. doi: 10.4088/JCP.11m06837 [DOI] [PubMed] [Google Scholar]
  36. Violanti JM, Fekedulegn D, Hartley TA, Charles LE, Andrew ME, Ma CC, & Burchfiel CM (2016). Highly rated and most frequent stressors among police officers: Gender differences. American Journal of Criminal Justice, 41(4), 645–662. 10.1007/s12103-016-9342-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. World Health Organization. (1998). Wellbeing measures in primary health care/The Depcare project. Copenhagen, Denmark: WHO Regional Office for Europe. [Google Scholar]
  38. Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, Dodds NE, Johnston KL, & Pilkonis PA (2012). Development of short forms from the PROMIS™ Sleep Disturbance and Sleep-Related Impairment item banks. Behavioral Sleep Medicine, 10(1), 6–24. doi: 10.1080/15402002.2012.636266 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets in the current study are available from the corresponding author on reasonable request.

RESOURCES