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Record W2410203642 · doi:10.1097/acm.0000000000000932

Choosing Wisely Canada

2015· letter· en· W2410203642 on OpenAlexaffabout
Marisa Leon-Carlyle, Raman Srivastava, Wendy Levinson

Bibliographic record

VenueAcademic Medicine · 2015
Typeletter
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsStewardship (theology)CurriculumMedical educationCuriosityResource (disambiguation)Critical thinkingPsychologyPublic relationsValue (mathematics)MedicinePedagogyPolitical scienceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

To the Editor: We were delighted to read about the success of the American Choosing Wisely campaign.1 Choosing Wisely Canada was launched on April 2, 2014 and similarly targets physicians and patients. As medicine recognizes the importance of resource stewardship in practice, there has been a congruent demand for the topic to be explicitly and implicitly included in medical curricula.2–4 Teaching students stewardship from the beginning of their education primes their medical knowledge and clinical thinking to ensure they are equipped to resist learning current wasteful medical practices. Most medical curricula do not celebrate restraint.4 Instead, medical education rewards thoroughness, curiosity, and searching for “zebras.” When students are asked to work up a patient, credit is awarded for naming tests of tangential clinical benefit. Associated costs and harms—such as increased anxiety, longer wait times, and unnecessary complications—are rarely discussed. Students learn to value overuse, the very problem stewardship aims to reduce. The underlying intention is logical—developing broad, critical thinking skills is vital for migrating students from book to bedside. Students must learn to develop differential diagnoses and investigate beyond typical presentations. We posit that teaching stewardship principles early contextualizes these lessons by bounding clinical searches with evidence-based knowledge. This thinking underlies Choosing Wisely Canada’s targeting of medical education. At the University of Toronto, stewardship content was increased by making small, but significant changes to existing cur ricula. We analyzed the undergraduate curricula, cataloged our experiences, and determined where stewardship naturally fit in existing education. We developed a spiral curriculum with distinct stew ardship learning objectives for each medical year. We then collaborated with administrators and mapped stewardship lessons where relevant. In the first year, stewardship is integrated in various lectures and seminars. Second-year lecturers are e-mailed relevant Choosing Wisely recommendations. Clerkship students attend stewardship seminars and learn value-based decision making in reframed internal medicine lessons. Similar to Wolfson and colleagues’ findings, the message of stewardship has strongly resonated with our faculty and students. The majority of lecturers have responded positively to our requests. Most importantly, student peers have begun to question low-value clinical decisions and weigh the risks and benefits of previously unquestioned interventions. Conversations have shifted from “What tests should we order?” to “What do we need to know?” To become a norm in medical practice, resource stewardship must be taught early in education. Updating curricula does not necessitate extra hours, and we hope our experiences can inspire similar curricula elsewhere. Marisa Leon-Carlyle MD candidate, Faculty of Medicine, University of Toronto, and medical student researcher, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada; [email protected] Raman Srivastava MD candidate, Faculty of Medicine, University of Toronto, and medical student researcher, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada. Wendy Levinson, MD Chair, Choosing Wisely Canada and Choosing Wisely International, professor, Department of Medicine, University of Toronto, and scientist, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0260.006

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.

Opus teacher head0.761
GPT teacher head0.588
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations56
Published2015
Admission routes2
Has abstractyes

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