Moving toward co-production: five ways to get a grip on collaborative implementation of Movement Behaviour curricula in undergraduate medical education
Bibliographic record
Abstract
Several "calls to action" have imposed upon medical schools to include physical activity content in their overextended curricula. These efforts have often neither considered medical education stakeholders' views nor the full complexity of medical education, such as competency-based learning and educational inflation. With this external pressure for change, few medical schools have implemented physical activity curricula. Moreover, Canada's new 24-Hour Movement Guidelines focus on the continuum of movement behaviours (physical activity, sedentary behaviour, and sleep). Thus, a more integrated process to overcome the "black ice" of targeting all movement behaviours, medical education stakeholder engagement, and the overextended curriculum is needed. We argue for co-production in curriculum change and offer five strategies to integrate movement behaviour curricula that acknowledge the complexity of the medical education context, helping to overcome our "black ice." Our objectives were to investigate 24-Hour Movement Guideline content in the medical curriculum and develop an integrated process for competency-based curriculum renewal. Stakeholders were equal collaborators in a two-phased environmental scan of 24-Hour Movement Guideline content in the Queen's University School of Medicine. Findings and a working curriculum map highlight how new, competency-based content may be embedded in an effort to guide more relevant and feasible curriculum changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.236 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.027 | 0.044 |
| Scholarly communication | 0.043 | 0.041 |
| Open science | 0.009 | 0.067 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".