An examination of trends in child sexual abuse investigations in Ontario over time
Bibliographic record
Abstract
BACKGROUND: Child sexual abuse (CSA) rates have been declining since the 1990s (Dunne et al., 2003; Finkelhor & Jones, 2004, 2012; Jones et al., 2001). Discrepancies in contexts and measures complicate comparing CSA rates across jurisdictions and studies, and there is limited literature about trends in CSA in Canada. OBJECTIVE: Using data from the Ontario Incidence Study of Reported Child Abuse and Neglect (OIS), the only source of provincially aggregated data in Ontario, Canada, that describes child welfare investigations, this paper provides information on reported and investigated CSA over the past 20 years. PARTICIPANTS AND SETTING: The OIS uses a file review methodology; information is collected directly from investigating child welfare workers. METHODS: A sample of child welfare agencies is selected for the study, and data are collected over a three-month period. Weights are applied to produce annual provincial estimates. RESULTS: The rates of investigated CSA in Ontario decreased between 1993 and 2013, from 5.20 (95% CI [3.94, 6.47]) to 1.81 (95% CI [0.97, 2.66]) children per 1000. During this time, the rate of all child maltreatment-related investigations doubled, from 21.41 (95% CI [18.38, 24.42]) to 53.32 ([29.61, 77.03]) children per 1000. CONCLUSIONS: Unlike other forms of child maltreatment, the incidence of investigated CSA in Ontario declined since 1993. Substantiation rates for CSA investigations decreased more dramatically than the rate of all CSA investigations, which could indicate a true decline in rate or an inability to accurately identify cases of CSA.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".