Effect of Social Support and Disclosure of Child Abuse on Adult Suicidal Ideation
Bibliographic record
Abstract
BACKGROUND: To examine the proportion of Canadian adults with a history of child abuse who disclosed the abuse to child protection services before age 16 years and identify the effect of social support and disclosure of child abuse on lifetime suicidal ideation. METHODS: Data for this study came from the Statistics Canada 2012 Canadian Community Health Survey-Mental Health (N = 9,076). Binary logistic regression was conducted to identify the effect of social support and disclosure of child abuse on suicidal ideation while simultaneously adjusting for the effect of type of child abuse and demographic, socioeconomic, health, and mental health factors. RESULTS: Of the 9,076 respondents who experienced at least one child abuse event, 21.5% reported ever experiencing suicidal ideation. Fewer than 6% of the respondents disclosed the abuse to someone from a child protection service before age 16 years. In the multivariate logistic regression model, respondents who disclosed the abuse to someone from child protection services were 1.37 times more likely to report lifetime suicidal ideation (95% CI, 1.10-1.71) than those who did not. Each additional unit increase in social support decreased the odds of lifetime suicidal ideation by a factor of 3% (95% CI, 0.95-0.98). CONCLUSIONS: Social support interventions that are effective in improving individuals' perception that support is available to them may help reduce suicidal ideation among those with a history of child abuse.
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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 | 0.010 |
| 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) | 0.003 | 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".