Childhood Abuse Prevalence in Canada: Insights from Six National Surveys
Bibliographic record
Abstract
Abstract Reliable estimates of childhood abuse (CA) prevalence are essential for informing resource allocation and prevention strategies. While previous research has provided valuable insights, inconsistencies in definitions and data collection methods have hindered a clear understanding of prevalence patterns. Recent changes in Canadian surveys enable comparisons of self-reported CA data, offering insight into prevalence over time and across survey contexts. The objective of this study is to estimate CA prevalence across six national surveys conducted in Canada from 2012 to 2022 and examine differences in prevalence estimates across surveys with distinct methodologies. Data were drawn from six population-based surveys conducted by Statistics Canada, including health-focused and victimization-focused surveys. The analysis was restricted to adults aged 25 and older living in the 10 Canadian provinces. Prevalence estimates were estimated using validated self-reported measures of childhood physical abuse, sexual abuse, and exposure to intimate partner violence (EIPV). Age-standardized estimates were compared across surveys. CA prevalence varied substantially across surveys, with health-focused surveys consistently reporting higher prevalence estimates than victimization-focused surveys. For example, in 2019, physical abuse prevalence was 28.4% (95% CI: 27.7–29.1) in the health survey versus 13.4% (95% CI: 12.7–14.1) in the victimization survey. Similar differences were observed for sexual abuse (10.9% vs. 7.1%) and EIPV (8.0% vs. 4.8%). Differences in prevalence estimates likely reflect variations in survey focus, question framing, and data collection methods rather than actual differences in CA experiences. Understanding these methodological variations is important for interpreting CA data accurately and improving cross-survey comparability.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".