Rates of out-of-home care among children in Canada: an analysis of national administrative child welfare data
Bibliographic record
Abstract
INTRODUCTION: As a part of the public health approach to child welfare, data about children placed in out-of-home care are needed to assess population trends, understand drivers of social and health inequities, and examine outcomes for children and families. We analyzed administrative data from Canada to describe the population of children in out-of-home care, and estimate and compare rates of out-of-home care by province/territory, year, sex/gender, age group and placement type. METHODS: We conducted a cross-sectional analysis of point-in-time data from all provinces and territories for the period 2013/2014 to 2021/2022. We used frequencies and percentages to describe the population of children (and youth up to age 21 years) in out-of-home care and estimated overall and stratified rates and rate ratios. RESULTS: An estimated 61 104 children in Canada were in out-of-home care on 31 March 2022. The national rate of out-of-home care was 8.24 children per 1000 population. Rate variations by province/territory were substantial and changed over time. Rates were highest among males and children aged 1 to 3 and 16 to 17 years. Foster homes were the most common type of placement, although kinship homes accounted for an increasing share. CONCLUSION: This analysis demonstrated that administrative data can be used to generate national indicators about children involved in the child welfare system. These data can be used for tracking progress towards health and social equity for children and youth in Canada.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".