Child abuse and mental disorders in Canada
Bibliographic record
Abstract
BACKGROUND: Nationally representative Canadian data on the prevalence of child abuse and its relation with mental disorders are lacking. We used contemporary, nationally representative data to examine the prevalence of 3 types of child abuse (physical abuse, sexual abuse and exposure to intimate partner violence) and their association with 14 mental conditions, including suicidal ideation and suicide attempts. METHODS: We obtained data from the 2012 Canadian Community Health Survey: Mental Health, collected from the 10 provinces. Respondents aged 18 years and older were asked about child abuse and were selected for the study sample (n = 23,395). The survey had a multistage stratified cluster design (household response rate 79.8%). RESULTS: The prevalence of any child abuse was 32% (individual types ranged from 8% to 26%). All types of child abuse were associated with all mental conditions, including suicidal ideation and suicide attempts, after adjustment for sociodemographic variables (adjusted odds ratios ranged from 1.4 to 7.9). We found a dose-response relation, with increasing number of abuse types experienced corresponding with greater odds of mental conditions. Associations between child abuse and attention deficit disorder, suicidal ideation and suicide attempts showed stronger effects for women than men. INTERPRETATION: We found robust associations between child abuse and mental conditions. Health care providers, especially those assessing patients with mental health problems, need to be aware of the relation between specific types of child abuse and certain mental conditions. Success in preventing child abuse could lead to reductions in the prevalence of mental disorders, suicidal ideation and suicide attempts.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".