The Sense of Coherence and Mental Health Among First Responders: a scoping review and critical analysis of causation
Bibliographic record
Abstract
Introduction: Public safety personnel (PSP) experience three to five times higher rates of occupatio-nal stress injuries and adverse mental health outcomes. Sense of coherence (SOC) is a psychological trait that reflects coping capacity to deal with stressors, shown to act as a protective factor against adverse mental health outcomes. Methods: This review was conducted using the five-stage scoping review framework to answer two questions: 1) what is the extent, nature, and quality of research that has investigated the SOC and PSP mental health, and 2) what is the current state of the causal inference between SOC and post-traumatic stress symptoms (PTSS) and how does this guide next steps for research. Articles were identified through seven databases, “grey” literature, and hand-searching. Publication details, me-thodology, participant information, and results were extracted and summarized using descriptive statistics. A quality and causal analysis were performed using the Quality in Prognostic Studies tool and Bradford Hill Criteria for causation. Results: Eleven articles were included in this review. The publication years ranged from 2000 to 2020, 91% of the articles originated from the European Union, and 64% had a medium to high risk-ofbias. The PSP population of interest included police officers, firefighters, and paramedics. Most studies (73%) used a cross-sectional design to assess the relationship between SOC and PTSS. All studies reported that lower PTSS were low to moderately associated with higher SOC scores (r= -0.17 to -0.47, p< 0.05 to 0.001 and β = -0.31, p= 0.009). There was insufficient research to support a causal relationship between the SOC and PTSS, as the literature did not support several criteria (i.e., strength, consistency, temporality, and experimental evidence). Conclusions: While SOC evidence is preliminary, associations are sufficient to warrant future longi-tudinal and interventional studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.038 | 0.025 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".