A Knowledge Translation Strategy to Promote the Health and Social Development of Students: An Evaluation Study
Bibliographic record
Abstract
Objective: In this paper, we evaluate the implementation of a knowledge translation strategy aimed at optimizing the use and deployment of the ÉKIP reference framework within both the education (preschool, primary, secondary) and the health and social services networks of the province of Quebec (Canada) and their partner organizations. Methods: We collected data on Web-based use of the reference framework (ÉKIP Online) and promotional newsletters in spring 2021. We then compared these with other data collected and analyzed in September 2022. We subsequently conducted 19 semi-structured interviews to explore the extent and nature of ÉKIP Online use, identify enabling and inhibiting factors, and extract recommendations for the project’s continuance. Results: The increase in use of ÉKIP Online and its uptake tools suggested that communities had become increasingly interested in and informed about the reference framework. Nevertheless, the qualitative component of the evaluation offered a nuanced perspective on its use and deployment. Conclusion: The evaluation documented a series of levers to ensure the greatest possible reach of the reference framework and its use within the networks. Further efforts are necessary to reach more schools and support use and deployment.
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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 | 0.062 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".