Police Peacekeeping: Health Risks and Challenges in a Post-Conflict Environment
Bibliographic record
Abstract
This study addressed the issue of negative outcomes experienced by police peacekeepers following work in a post-conflict environment. Responses from nearly 600 active and retired police officers who had participated in at least one peacekeeping mission, were analysed to determine baseline data on a range of work, interpersonal, and family issues experienced by police peacekeepers. The data from the present survey were also compared with previous sick-leave data collected as part of a work attendance management project. The results suggest that police peacekeepers have relatively few negative outcomes following the mission, that the level of extended sick leave is lower for peacekeepers than for personnel who have not been on a peacekeeping mission, and that the average number of sick days taken by peacekeepers does not change significantly following a peacekeeping mission. While alcohol consumption increases for peacekeepers during the mission, it returns to normal rates for most individuals following repatriation. The study concludes that participating in a peacekeeping mission does not pose an inordinate risk for police officers, and is a positive experience for many. In general, the screening and selection process appears to be working very well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".