The association between adverse childhood experiences (ACEs) and suicide attempts in a population‐based study
Bibliographic record
Abstract
OBJECTIVES: To further our understanding of the relationship between Adverse Childhood Experiences (ACEs) and suicidal behaviour, this study investigates the association between three types of ACEs and lifetime suicide attempts, while considering potential gender-specific and mediating effects. METHODS: Data were obtained from the 2012 Canadian Community Health Survey-Mental Health (CCHS-MH), a cross-sectional, population-based survey comprised of respondents aged 18 or older who provided self-reported data on past experiences of suicide attempts, as well as childhood sexual abuse (CSA), childhood physical abuse (CPA) and parental domestic violence (PDV) (n = 22 559). After testing for ACE by gender interactions, we estimated the odds of lifetime suicide attempts for each ACE and then investigated whether depression, anxiety, substance abuse and chronic pain acted as mediators of the relationship. RESULTS: The odds of suicide attempts are significantly higher among those with a history of CPA (OR = 3.29; 99.9% CI 2.33-4.64), CSA (OR = 4.42; 99.9% CI 3.14-6.23) or PDV (OR = 2.52; 99.9% CI 1.69-3.76), when ACEs are mutually adjusted. There is little evidence that gender acts as a moderator; however, depression, anxiety, substance abuse and chronic pain appear to partially mediate the associations. Depression alone accounts for about a quarter of the associations with CSA and CPA. CONCLUSIONS: Mental health factors and chronic pain appear only to partially mediate relationships between ACEs and lifetime suicide attempts. Future research should look at other pathways with the goal of developing multi-level interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".