When services are no longer voluntary: Exploring child protection workers experiences with Black families and (in)voluntary services
Bibliographic record
Abstract
BACKGROUND: While the overrepresentation of Black families in contact with the child welfare system is a well-established, how child welfare workers specifically engage Black families in voluntary services is poorly understood in Canada. OBJECTIVE: This study aims (1) to explore workers' experiences of Black families engaging with voluntary services?; and (2) understand how workers' perceptions shape decision making practices as it relates to voluntary services. METHODS: An interpretive phenomenological analysis approach is employed drawing on data collected as part of the Mapping Disparities for Black Families Project (MDBF). Data was collected via in-depth semi-structured interviews and focus groups with a total of 79 participants from April 2022 to January 2023. FINDINGS: Results from this study reveal racial differences between Black and white families in child welfare practices related to consent and use of legal sanctions. Study findings also uncover a pattern of documenting and perceiving Black families as 'aggressive' in child welfare. Lastly, it was found that workers who effectively reframed perceived anger and other emotions in their interactions with Black families had greater engagement success. CONCLUSION: Implications of this study highlight the fallacies of consent and voluntary services in child welfare. Findings from this study also suggest that narratives of Black families begin to form even before Black families are formally inducted into the child welfare process due to not only worker's preconceived bias ideas but also prejudicial reporting that is then reinforced by worker bias. Policy and practice recommendations are discussed.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".