Creating an EBP Framework on a Journey to Becoming an EBP Agency: Pioneers in the Field of Children's Mental Health
Bibliographic record
Abstract
Agencies servicing children, youth, and families have been particularly pressured to demonstrate service effectiveness and accountability by government funders. The human service fields have not fully embraced research evidence into the organizational culture creating a challenge of introducing research evidence into agencies. Gaps in knowledge have been identified when agencies attempt to travel down the path of introducing evidence-based practice into organizational culture. The paradigm shift of introducing research into practice was the journey taken by one mid-sized agency in southwestern Ontario, Canada. A framework for assessing evidence-based practice programs in services was created as part of their journey.
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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 | 0.103 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.030 | 0.057 |
| Scholarly communication | 0.038 | 0.030 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".