Testifying after an Investigation: Shaping the Mental Health of Public Safety Personnel
Bibliographic record
Abstract
In this editorial, we draw on two Canadian cases to interrogate how mass causality events and investigations consume many responders before (e.g., public safety communicators, detachment service assistants), during (e.g., police, fire, paramedics), and after the incident (e.g., coroners, correctional workers, media coverage). Their well-being may suffer from the associated processes and outcomes. In the current article, we focus on the mass causality incident of 2020 in Nova Scotia, Canada, and the investigation following a prisoner death in 2019 in Newfoundland, Canada, to explore how testifying post-incident can be made more palatable for participating public safety personnel (PSP). Specifically, we study how testifying after an adverse event can affect PSP (e.g., recalling, vicarious trauma, triggers) and how best to mitigate the impact of testimony on PSP well-being, with a lens to psychological "recovery" or wellness. We focus here on how to support those who may have to testify in a judicial proceeding or official inquiry, given being investigated for best-intended actions can result in moral injury or a posttraumatic stress injury, both exacerbated by judicial review, charge, accusation, or inquiry.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| 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".