Examining Child Welfare Outcomes for Asian-Canadian Children and Families: A Mixed Methods Study
Bibliographic record
Abstract
This three-paper dissertation triangulated three different data sources using mixed methods to build a comprehensive understanding of Asian-Canadian households involved in the child welfare system at the national and local levels. The first paper used a mixed method approach to build a descriptive profile of Asian-Canadian households involved in the child welfare system. The results from secondary data analysis using the 2008 Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2008) indicated substantive differences between Asian-Canadian and White-Canadian households investigated by child welfare agencies. These results were presented to focus groups consisting of child protection workers and community service providers, eliciting practice insights and improved understanding of child welfare decision-making. The second paper compared child maltreatment investigations in the CIS-2008 to Canadian Census child population data. The study found that Asian-Canadian households were under-represented in the child welfare system and had almost two times the odds of case closure after an investigation compared to White-Canadian households. Three different disparity indexes were used in the analyses: population-based, decision-based, and maltreatment-based. The results demonstrated the need for greater clarity and consistency in the definitions and methodology for examining racial disparity in child welfare research. The third paper used administrative child welfare data from the Ontario Child Abuse and Neglect Data System (OCANDS) to examine the decision to close after investigation rather than transfer to ongoing child protection services (CPS). Compared to White-Canadian households involved in the child welfare system, Asian-Canadian households received ongoing CPS for over a month longer and were almost half as likely to be re-investigated within one year after case closure. This suggests that child protection investigations involving Asian-Canadian households may not be closed prematurely, but rather, provided the necessary intervention. Together, the three papers examine the profile of Asian-Canadian households in the child welfare system as well as child welfare decision patterns and services provided to this group. The discussion section for each paper and the dissertation's conclusion summarizes the study results, limitations, and implications for social work and child welfare research, theory and practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".