Is Peacekeeping Peaceful? A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature on the association between deployment to a peacekeeping mission and distress, mental disorders, and suicide. METHODS: Peer-reviewed English publications were found through key word searches in MEDLINE, PsycINFO, Scopus, and Embase, and by contacting authors in the field. Sixty-eight articles were included in this review. RESULTS: Some studies have found higher levels of postdeployment distress and posttraumatic stress disorder (PTSD) symptoms. Most studies have not shown an increased risk of suicide in former peacekeepers. Correlates of distress and PTSD symptoms included level of exposure to traumatic events during deployment, number of deployments, predeployment personality traits or disorder, and postdeployment stressors. Perceived meaningfulness of the mission, postdeployment social supports, and positive perception of homecoming were associated with lower likelihood of distress. CONCLUSIONS: Most peacekeepers do not develop high levels of distress or symptoms of PTSD. As postdeployment distress is consistently shown to be associated with high levels of exposure to combat during deployment, targeted interventions for peacekeepers who have been exposed to high levels of combat should be considered.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".