Firefighters experience high rates of posttraumatic stress disorder (PTSD). It is imperative to identify malleable factors that protect against the development of PTSD symptoms among this population. We examined whether perceptions of belongingness broadly (Study 1) and social support from supervisors, coworkers, and family/friends specifically (Study 2) are associated with lower PTSD symptom severity among firefighters. Study 1 included 840 U.S. firefighters (91.1% male); participants completed the Interpersonal Needs Questionnaire and PTSD Checklist-Civilian Version. Study 2 included 200 U.S. women firefighters exposed to a Criterion A traumatic event; participants completed the Generic Job Stress Questionnaire, Life Events Checklist for Diagnostic and Statistical Manual of Mental Disorders-5, and PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders-5. Linear regression analyses were conducted, adjusting for the number of years participants served as firefighters. Greater belongingness broadly (Study 1; b = -0.740, p < .001) as well as social support specifically (Study 2) from supervisors (b = -4.615, p < .001), coworkers (b = -4.465, p = .001), and family/friends (b = -3.206, p = .021) were associated with less severe PTSD symptoms. When all sources of social support were entered into a single model, only support from supervisors was significantly associated with lower overall PTSD symptom severity (b = -4.222, p = .004). Belongingness and social support may protect against the development of PTSD among firefighters. Supervisor social support may be particularly salubrious, suggesting that top-down mental wellness promotion within the fire service may be indicated to protect firefighters against PTSD. (PsycINFO Database Record

Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched theories of suicide (i.e., BioSocial, Biological, Ideation-to-Action, and Hopelessness Theories) to machine learning models, comparing the accuracy between the two differing approaches. We conducted literature searches using PubMed, PsycINFO, and Google Scholar, gathering effect sizes from theoretically-relevant constructs and machine learning models. Eligible studies were longitudinal research articles that predicted suicide ideation, attempts, or death published prior to May 1, 2020. 124 studies met inclusion criteria, corresponding to 330 effect sizes. Theoretically-driven models demonstrated suboptimal prediction of ideation (wOR = 2.87; 95% CI, 2.65–3.09; k = 87), attempts (wOR = 1.43; 95% CI, 1.34–1.51; k = 98), and death (wOR = 1.08; 95% CI, 1.01–1.15; k = 78). Generally, Ideation-to-Action (wOR = 2.41, 95% CI = 2.21–2.64, k = 60) outperformed Hopelessness (wOR = 1.83, 95% CI 1.71–1.96, k = 98), Biological (wOR = 1.04; 95% CI .97–1.11, k = 100), and BioSocial (wOR = 1.32, 95% CI 1.11–1.58, k = 6) theories. Machine learning provided superior prediction of ideation (wOR = 13.84; 95% CI, 11.95–16.03; k = 33), attempts (wOR = 99.01; 95% CI, 68.10–142.54; k = 27), and death (wOR = 17.29; 95% CI, 12.85–23.27; k = 7). Findings from our study indicated that across all theoretically-driven models, prediction of suicide-related outcomes was suboptimal. Notably, among theories of suicide, theories within the Ideation-to-Action framework provided the most accurate prediction of suicide-related outcomes. When compared to theoretically-driven models, machine learning models provided superior prediction of suicide ideation, attempts, and death.

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