Does mandatory reporting legislation increase contact with child protection? – a legal doctrinal review and an analytical examination
Bibliographic record
Abstract
BACKGROUND: Within Canadian provinces over the past half-century, legislation has been enacted to increase child protection organization (CPO) involvement in situations of child maltreatment (CM). This study had two objectives: 1) to document enactment dates of legislation for mandatory reporting of CM; 2) to examine reported CPO involvement among people reporting a CM history in relation to the timing of these legislative changes. METHODS: The history of mandatory reporting of CM was compiled using secondary sources and doctrinal legal review of provincial legislation. The 2012 Canadian Community Health Survey - Mental Health (CCHS-MH) with n = 18,561 was analyzed using birth cohorts to assess associations between the timing of legislation enactment and contact with CPO. RESULTS: All Canadian provinces currently have mandatory reporting of physical and sexual abuse; 8 out of 10 provinces have mandatory reporting for children's exposure to intimate partner violence. Increases in reporting CM to CPOs paralleled these laws' enactment, particularly for severe and frequent CM. CONCLUSIONS: These findings show that mandatory reporting laws increase reporting contact with CPO, particularly for severe and frequent CM. Whether they have had the intended effect of improving children's lives remains an important, unanswered question.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".