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Record W3013701123 · doi:10.1186/s12909-020-1938-7

Addressing the health advocate role in medical education

2020· article· en· W3013701123 on OpenAlexaffabout
Suzanne Boroumand, Michael J. Stein, Mohammad Jay, Julia W. Shen, Michael Hirsh, Shafik Dharamsi

Bibliographic record

VenueBMC Medical Education · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHealth careAccountabilityHealth equityMedical educationPublic relationsEquity (law)Social accountingSocial determinants of healthHealth policyMedicinePublic healthPolitical scienceNursingBusiness

Abstract

fetched live from OpenAlex

The health advocate role is an essential and underappreciated component of the CanMEDs competency framework. It is tied to the concept of social accountability and its application to medical schools for preparing future physicians who will work to ensure an equitable healthcare system. Student involvement in health advocacy throughout medical school can inspire a long-term commitment to address health disparities. The Social Medicine Network (SMN) provides an online platform for medical trainees to seek opportunities to address health disparities, with the goal of bridging the gap between the social determinants of health and clinical medicine. This online platform provides a list of health advocacy related opportunities for addressing issues that impede health equity, whether through research, community engagement, or clinical care.First implemented at the University of British Columbia, the SMN has since expanded to other medical schools across Canada. At the University of Ottawa, the SMN is being used to augment didactic teachings of health advocacy and social accountability. This article reports on the development and application of the SMN as a resource for medical trainees seeking meaningful and actionable opportunities to enact their role as health advocates.

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)0.002
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.441
Teacher spread0.363 · 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.

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

Citations59
Published2020
Admission routes2
Has abstractyes

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