Economic Evaluation of Proactive PTSI Mitigation Programs for Public Safety Personnel and Frontline Healthcare Professionals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Public safety personnel and frontline healthcare professionals are at increased risk of exposure to potentially psychologically traumatic events (PPTEs) and developing post-traumatic stress injuries (PTSIs, e.g., depression, anxiety) by the nature of their work. PTSI is also connected to increased absenteeism, suicidality, and performance decrements, which compromise occupational and public health and safety in trauma-exposed workers. There is limited evidence on the cost effectiveness of proactive "prevention" programs aimed at reducing the risk of PTSIs. The purpose of this meta-analysis is to measure the economic effectiveness of proactive PTSI mitigation programs among occupational groups exposed to frequent occupational PPTEs, focusing on the outcomes related to PTSI symptoms, absenteeism, and psychological wellness. Findings from 15 included studies demonstrate that proactive interventions can yield substantial economic and health benefits, with Return On Investment (ROI) values ranging widely from -20% to 3560%. Shorter interventions (≤6 months) often produced higher returns, while longer interventions (>12 months) showed more moderate or negative returns. Notably, the level at which an intervention is targeted significantly affects outcomes-programs aimed at managers, such as the 4 h RESPECT training course, demonstrated a high ROI and broad organizational impact by enhancing leadership support for employee mental health. Sensitivity analyses highlighted significant variability based on the organizational context, program design, and participant characteristics. The majority of proactive interventions successfully reduced psychological distress and enhanced workplace outcomes, although thoughtful consideration of program design and implementation context is essential.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".