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Record W4225311743 · doi:10.1177/23821205221082913

Developing a Curriculum for Addressing the Opioid Crisis: A National Collaborative Process

2022· article· en· W4225311743 on OpenAlexaffabout
Klodiana Kolomitro, Lisa Graves, Fran Kirby, Jennifer Turnnidge, Amber Hastings Truelove, Nancy Dalgarno, Richard van Wylick, Denise Stockley, Jeanne Mulder

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSt. John’s Health Sciences CentreQueen's University
Fundersnot available
KeywordsCurriculumMedical educationStakeholderMedicineStewardship (theology)Process (computing)Curriculum developmentEngineering ethicsPublic relationsPsychologyPolitical sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The burgeoning use of opioids and the lack of attention to the safe prescribing, storage, and disposal of these drugs remains a societal concern. Education plays a critical role in providing a comprehensive response to this crisis by closing the training gaps and empowering the next generation of physicians with the knowledge, skills, and resources needed to diagnose, treat and manage pain and substance use. Curricular Development: The Association of Faculties of Medicine of Canada (AFMC) developed a competency-based, bilingual curriculum for undergraduate medical students to be implemented in all Canadian medical schools. The authors describe the principles and framework for developing a national curriculum. The curriculum design process was situated in the Knowledge to Action theoretical framework. Throughout the development of this curriculum, different stakeholder groups were engaged, and their needs and contexts were considered. CONCLUSION: The curriculum ensures that consistent information is taught across all medical schools to educate future physicians on pain management, opioid stewardship and substance use disorder.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.381
Teacher spread0.351 · 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 teacher head, not a consensus.

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

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

Citations11
Published2022
Admission routes2
Has abstractyes

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