Child abuse prevalence estimates in Canada; comparisons of nationally representative data from 2012 to 2022: a population-based study
Bibliographic record
Abstract
Background: Up-to-date nationally representative Canadian statistics on child abuse with a focus on sex, sexual identity, and age cohorts are overdue. The objective of the current study was to examine child abuse prevalence estimates (physical abuse, sexual abuse, exposure to intimate partner violence (EIPV), and any child abuse) among adult Canadians, associations with sex (male or female), sexual identity (heterosexual, lesbian or gay, bisexual, or other), and age cohort, and to compare data from 2022 with 2012. Methods: Data were obtained from two Statistics Canada cross-sectional surveys: 1) the 2012 Canadian Community Health Survey-Mental Health (2012 CCHS-MH; n = 23,395; 18+ years) and 2) the 2022 Mental Health and Access to Care (2022 MHACS; n = 9409; 18+ years). Findings: The prevalence of any child abuse in Canada in 2022 was 34.4%, which was significantly higher compared to 2012 (32.1%; p = 0.006). Among the youngest respondents (18-27 years), the prevalence of any child abuse had also increased from 21.7% in 2012 to 26.8% in 2022 (p = 0.002). Sex and age cohort differences were noted. In addition, those identifying as other than heterosexual generally had increased odds of child abuse experiences (Adjusted Odds Ratios ranging from 1.48 to 3.12). Interpretation: The retrospective self-reported prevalence in 2022 was 2.3 percentage points higher compared to 2012. There continues to be a widespread need to develop approaches focusing on child abuse prevention and response, and to ensure that providers receive training in how to recognize and respond safely to family violence, including child abuse. Funding: Canadian Institutes of Health Research and Canada Research Chair.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".