Lives saved: Correctional officers’ experiences in the prevention of prisoner death by suicide
Bibliographic record
Abstract
Few researchers have empirically examined completed or attempted death by suicide in prison and, of the available research, most has been conducted in the United Kingdom. Our purpose in this article is to increase awareness and knowledge about “lives saved” in custody drawing on the voices of Canadian provincial and territorial correctional officers (COs; n = 43). We investigate their experiences with attempted and completed death by suicide to shed light on the commonality of such experiences, and their impact on officers over time. Based on an analysis of semi-structured interview transcripts, we explore the effects of exposure to these potentially psychologically traumatic events on COs, specifically their emotional responses, as well as the supports available, both informal and formal, as they navigate their work. We found that the roles of officers, which frequently overlap with the domains of mental health support and first response in a crisis situation, are often under-recognized and underappreciated. Ultimately, we highlight the need for increased dialogue surrounding workplace trauma and mental health in prison and explore the institutional barriers that arise when facilitating discussion about mental health. We show that although some lives are lost in prison, due to natural and nonnatural causes, many lives are saved by prison staff.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".