Bibliographic record
Abstract
Purpose The purpose of this study is to examine potential predictors of suicidal ideation among a large sample of Norwegian police officers. Some have suggested that suicide is a leading cause of death among police officers. Design/methodology/approach Data were collected using anonymously completed questionnaires from 766 officers, a 60 percent response rate most measures included were commonly used by other researchers. Predictors included personal demographics, work situation characteristics, job demands, burnout components, work outcomes and coping responses. Logistic regression analysis was used as the prevalence of suicidal ideation was strongly skewed; most police officers indicated no suicidal ideation. Two criterion groups were created; police officers indicating no suicidal ideation (n=495) and police officers indicating some suicidal ideation (n=124). Findings Single police officers, officers reporting higher levels of both exhaustion and cynicism (burnout components), and officers engaging in less active coping and reporting lower levels of social support indicated more suicidal ideation. Research limitations/implications Use of self‐report data raises the possibility of response set tendencies. Practical implications Organizations can undertake efforts to prevent potential suicide of their members. It appears that reducing levels of burnout, increasing social support, and highlighting the benefits of active coping represent useful starting points. Originality/value This study contributes to our understanding of suicidal ideation among police officers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".