112 Tackling overuse starts with what we share and teach: ten recommendations for doing better
Bibliographic record
Abstract
This seminar will explore 10 recommendations for tackling low-value care through healthcare education and knowledge translation. Despite the negative impact of low-value care and overdiagnosis, medical education and conference scientific committees rarely ensure that their material does not lead to overuse and that the judicious use of healthcare resources is promoted. Through a collaborative effort with stakeholders, 10 actionable recommendations were developed to foster high-value care. Educators should thus rely on evidence-based decision-making, transparency, and the inclusion of resource stewardship in medical curricula and continuous professional development. Attendees will review the recommendations and discuss their application to promote safer and more efficient healthcare practices. Objectives Review the 10 recommendations to foster high-value care in health professionals education and knowledge translation Identify ways these recommendations may be adapted to local needs Share ideas to bring this discussion forward in different contexts Method We will share how these recommendations were developed and how they influence some aspects of medical education or conferences in Quebec. After a short presentation, we will engage the audience in a discussion about these recommendations and their applicability in the participant’s context. Results The 10 recommendations were published in the BMJ Evidence-based Medicine Journal and were adopted by continuing medical education accreditors in Quebec. Conclusions The recommendations can be used to foster high-value care through all forms of teaching and knowledge translation.
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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 | 0.073 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".