Association between a history of child abuse and suicidal ideation, plans and attempts among Canadian public safety personnel: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: A history of child abuse has been identified as a risk factor for suicidal behaviour in general population samples; however, it remains unknown how a history of child abuse and career-related trauma together are related to suicidal behaviour. This cross-sectional survey was designed to 1) estimate the prevalence of a history of child abuse among Canadian public safety personnel, 2) examine the associations between child abuse and suicidal behaviour, 3) examine the associations between career-related trauma and suicidal behaviour and 4) examine the cumulative and interactive effects of child abuse and career-related trauma on suicidal behaviour. METHODS: Data were drawn from a Web-based survey collected by the Canadian Institute for Public Safety Research and Treatment Team. Child abuse included physical abuse, sexual abuse and exposure to intimate partner violence in childhood. Suicidal behaviour included lifetime ideation, plans and attempt(s). We used logistic regression models to examine the associations between child abuse and suicidal behaviours, and cumulative and interaction models to test the relations between a history of child abuse and career-related trauma on suicidal behaviours. RESULTS: = 4199). A total of 2275/4073 respondents (55.9%) reported experiencing 1 or more types of abuse as a child. All types of child abuse and career-related trauma were significantly associated with suicidal behaviour (adjusted odds ratio 1.57-3.25). No cumulative or interaction effects were noted. INTERPRETATION: Both a history of child abuse and career-related trauma were significantly associated with suicidal behaviours; however, stronger relations were seen for the former. This finding may help the development of effective treatment and intervention strategies aimed at reducing suicidal behaviour among public safety personnel.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".