Focusing on Uptake: The Evolution of an Evidence-Informed Classroom Resource for Student Mental Health
Bibliographic record
Abstract
It is widely recognized that the most effective student mental health interventions, tools, and resources are those that are solidly grounded in theory, evidence, and practice. But developing interventions in this way can be a time-consuming, challenging process. This article describes the process of developing a classroom resource to build social emotional learning skills among high school students in Ontario. The resource was informed by the latest research evidence while also being sensitive to the implementation context and needs of educators and students. In creating, evaluating, and revising these resources over several years, lessons have emerged about what it takes to navigate inherent challenges, balance competing needs and priorities, and ultimately develop an intervention that is both evidence-informed and implementation sensitive. Flexible funding, effective partnerships, and a commitment to contextual responsivity are key.
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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 | 0.195 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.012 | 0.033 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".