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Record W4205102419 · doi:10.22454/fammed.2022.598895

Students of Change: Health Policy in Action

2022· article· en· W4205102419 on OpenAlexfundno aff

Bibliographic record

VenueFamily Medicine · 2022
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
FundersDepartment of Family and Community Medicine, University of Toronto
KeywordsAction (physics)CurriculumHealth policyTest (biology)Call to actionHealth education

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Health policy is more impactful for public health than many other strategies as it can improve health outcomes for an entire population. Yet in the "see one, do one, teach one" environment of medical school, most students never get past the "see one" stage in learning about the powerful tools of health policy and advocacy. The University of New Mexico School of Medicine mandates health policy and advocacy education for all medical students during their family medicine clerkship rotation. The aim of this project is to describe a unique health policy and advocacy course within a family medicine clerkship. METHODS: We analyzed policy briefs from 265 third-year medical students from April 2016 through April 2019. Each brief is categorized by the level of change targeted for policy reform: national, state, city, or university/school. Implemented policies are described. RESULTS: Slightly less than one-third of the policies (30%) relate to education, 36% advocate for health system change by addressing cost, access, or quality issues, and 34% focus on public health issues. Fourteen policies have been initiated or successfully enacted. CONCLUSIONS: This curriculum gives each medical student a health policy tool kit with immediate opportunities to test their skills, learn from health policy and advocacy experts, and in some cases, implement health policies while still in medical school. A 1-week family medicine policy course can have impact beyond the classroom even during medical school, and other schools should consider this as a tool to increase the impact of their graduates.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.216
GPT teacher head0.478
Teacher spread0.262 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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