NAVIGATING “SAFETY” IN A PANDEMIC: A CRITICAL EXAMINATION OF ONTARIO CHILD WELFARE SAFETY INTERVENTIONS FOR NEWCOMER PARENTS AND OTHER FAMILIES DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
This study investigated circumstances surrounding the impact of COVID-19 on child protection investigations, particularly those affecting newcomer parents in Ontario, Canada. Recognizing that the pandemic inflicted substantial socioeconomic disadvantage on some people, the purpose of the study was to use an intersectional lens to examine challenges and solutions found by child welfare agencies when working with families. Insights for policy and practice are drawn from 11 virtual interviews with child welfare workers and managers in Ontario. Our findings reveal that some newcomer families encountered unique challenges: ineligibility for the available pandemic public assistance; inaccessibility to faith-based supports, which had often been their first key contact for mental wellness in the past; technological inequities; and language barriers. These intersecting conditions impacted newcomer families and led to innovative child protection interventions. Analysis of the interview data shows a gradual shift in Ontario from risk-focused approaches to supportive and preventative child welfare interventions in families. Furthermore, supervisors faced the dilemma of how stringently to enforce ongoing safety policies when some social workers were questioning the benefits of these rules for families with intersecting identities who were experiencing added burdens because of the pandemic.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".