Are we there yet? Evaluation and the knowledge translation journey.
Bibliographic record
Abstract
INTRODUCTION: Critical to knowledge translation are organizations' efforts to evaluate their implementation of evidence-based practices (EBPs). Organizations face challenges in their ability to be aware of emerging practices, to measure their efforts against current evidence, and to adapt EBPs to their contextual environments. The Provincial Centre of Excellence for Child and Youth Mental Health has engaged in initiatives to increase the uptake of EBPs and mobilize knowledge by building capacity for evaluation and research in the sector. METHODS: Consultation services and innovative grants to organizations with mental health programs and services, where the Centre acts as both knowledge and relationship broker, are contributing to organizations' capacity to do and use evaluation. RESULTS: Case exemplars illustrate the processes, successes and challenges experienced by organizations in Centre-supported activities. The Centre's efforts to build organizations' skills in doing and using evaluation, promoting a learning-by-doing approach and fostering collaboration are described. CONCLUSIONS: Organizations with the capacity to conduct effective evaluations are better able to implement and assess EBPs, conduct quality evaluations, and contribute to research in the child and youth mental health sector. Widespread gains in mental health organizations' evaluation capacities will contribute to system innovations and the fostering of collaborative partnerships.
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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 | 0.185 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".