Successful, sustainable? Facilitating the growth of family group conferencing in Canada
Bibliographic record
Abstract
Family-centered approaches offer significant promise regarding the enhancement of child and family safety. Child protection workers find value in working alongside families, whereas families appreciate having a voice in decision-making processes. The introduction of Family Group Conferencing (FGC) in New Zealand in 1989 prompted the exploration of family partnership options internationally. This study, focusing on Ontario, Canada, examines the expansion of FGC from a local pilot in 1998 to a current province-wide initiative. The internal and external factors that have promoted and inhibited change were investigated. Interviews and a focus group were used to elicit the perspectives of Alternative Dispute managers and key informants. Participants concluded that the FGC program has been successful as a result of multilevel and multipronged change efforts. However, the long-term viability remains in question, primarily because of unstable funding and uneven buy-in, on provincial levels and within child welfare agencies. To ensure sustainability current strengths should be built upon, employing the same intentional, strategic planning that characterized the introduction of FGC into the province. The Ontario experience provides pointers for interested parties wishing to embed FGC and other family-centered approaches in daily child welfare practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".