Inevitable Loss and Prolonged Grief in Police Work: An Unexplored Topic
Bibliographic record
Abstract
The present manuscript presents foundational constructs related to death and loss (i.e., grief, bereavement, prolonged grief) providing empirical findings from recent research on the impact of death and loss on police officers' health, behavior, and overall functioning. Police officers are routinely exposed to death. In many instances, officers' contact with decedents includes, among others, victims of accidents, catastrophes, or violent crimes and witnessing the intense emotional suffering of relatives of the deceased. Additionally, it is not uncommon for officers to experience the loss of fellow officers from on-duty deaths and permanent, career-ending injuries. Simultaneously, like everyone, police officers have to cope with deaths of loved ones in their personal lives. The result is that officers' health and well-being are likely compromised because of the systematic exposure to on- and off-duty deaths. In this perspective paper, death and loss in law enforcement are explored in an attempt to raise awareness and increase attention to this area of police work. In addition, the authors list a number of prophylactic intervention strategies that would support officers cope with the impact of loss and death and promote their own resilience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| 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".