Using Research within Child Welfare: Reactions to a Training Initiative
Bibliographic record
Abstract
PURPOSE: Efforts to incorporate evidence-informed practice within child welfare have been increasingly adopted to promote positive outcomes for youth. We established partnerships with three child welfare agencies to develop, implement, and evaluate a training curriculum delivered to senior managers and supervisors. The training focused on the use of data from an Ontario performance measure system. Despite its mandatory use, challenges remain in the applied use of the data to organizational governance and planning. METHOD: This pilot study examined senior managers' and supervisors' perspectives of the training using a mixed-methods design consisting of a training feedback questionnaire and post-training focus groups. RESULTS: Results indicated that participants responded positively to the training content, delivery, and facilitators. Participants identified that it was helpful to learn about applied data and evidence-informed practice. CONCLUSION: These findings highlight the importance of ongoing training initiatives within child welfare to promote an organizational culture supportive of evidence-informed 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 | 0.133 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".