After 10–7: trauma, resilience and satisfaction with life among retired police officers
Bibliographic record
Abstract
Purpose Most research on trauma, resilience and well-being among police officers focusses on those still on active duty. Comparatively speaking, and despite an aging workforce and established negative health outcomes, similar inquiries involving police retirees are not as common. The purpose of this paper is to examine the effects of on- and off-the-job trauma and resilience on satisfaction with life among a sample of retired police officers. Design/methodology/approach Data were collected via a cross-sectional nonprobability electronic survey of police retirees in Ontario, Canada. While controlling for employment-related variables and demographic characteristics, a series of hierarchical multiple regression models were used to examine the effects of on- and off-the-job trauma and resilience on satisfaction with life among a sample of 932 participants. Findings The analysis indicates that off-the-job trauma and both personal and social dimensions of resilience contribute uniquely to satisfaction with life among police retirees. Moreover, this full model explains nearly 37% of the variance in satisfaction with life. Originality/value This study's findings offer further direction to those working to support the health and well-being of officers currently on the job and those well into their retirement years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".