A Longitudinal Study to Better Understand Child Protection Intervention for First Nations Children
Bibliographic record
Abstract
This study brings forward new evidence regarding child protection (CP) intervention for First Nations children and contributes to a longitudinal understanding of their trajectories within CP services. It raises questions regarding the persisting, unmet needs of First Nations children, families, and communities by identifying the CP factors associated with a first decision to provide post-investigation intervention and a first decision to close a case following post-investigation intervention among First Nations children. Anonymized administrative data (2002–2014; n = 1340) were used to conduct multivariate analyses, including longitudinal analyses using Cox proportional hazards modeling. Among First Nations children, those who were very young, who were reported for serious risk of neglect, and whose situation included indicators of repeated individual or family contact with CP services were more likely to receive post-investigation intervention. Similarly, those who were very young, provided services for neglect or serious risk of neglect, and whose situation was investigated at least twice before intervention was provided were more likely to have a longer first episode of intervention. The longitudinal analyses also revealed that more than one in two First Nations children (51.7%) receiving post-investigation intervention experienced a placement in out-of-home care during their interaction with CP services. This study contributes to a better understanding of intervention for First Nations children in Canada. It highlights how First Nations children receiving CP intervention live in situations in which their needs persist over time and how current services do not appear able to respond to these situations adequately, supporting the move towards autonomous, Indigenous–led CP services.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".