Non-English/non-French Speaking Caregivers Involved with the Canadian Child Welfare System: Findings from the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2008)
Bibliographic record
Abstract
Objective: The objective is to provide a profile of non-English/non-French speaking families investigated by child welfare, with primary caregiver language acting as a proxy for immigration. This analysis examines the impact of language on service disposition. Methods: Multivariate analysis was conducted to determine whether primary caregiver language impacts the decision to transfer a case to ongoing services at the conclusion of the investigation, after controlling for clinical factors. Results: Investigations involving non-English/non-French speaking caregivers were more likely to identify physical abuse as the primary maltreatment form, more likely to indicate the caregiver has few social supports and is a victim of domestic violence, and more likely to report no primary source of income than investigations involving non-immigrant caregivers. When controlling for clinical factors, investigations involving these caregivers were significantly less likely to be transferred to ongoing services. However, when controlling for language and clinical factors, investigations of physical abuse were significantly less likely to be opened for ongoing services than investigations of all other maltreatment types. Conclusions and Implications: The findings suggest that there is an interaction between primary caregiver language and maltreatment type in predicting transfers to ongoing services. Given the lower risk profiles of non-English/non-French speaking families, although concerns of social isolation and domestic violence were more likely to be noted, a possible explanation is that these families are overrepresented in investigations of physical abuse. The potential utility of parenting education programs tailored immigrant families as one avenue to address the problem of abusive discipline, merits research attention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".