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Record W2097691551 · doi:10.3109/0142159x.2010.494740

Health advocacy training: Why are physicians withholding life-saving care?

2010· article· en· W2097691551 on OpenAlexafffundabout
Peter J. Gill, Harbir Gill

Bibliographic record

VenueMedical Teacher · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHealth Sciences CentreUniversity of Alberta
FundersUniversity of Calgary
KeywordsCommitCurriculumHealth carePopulation healthHealth policyPublic healthMedical educationMedicinePolicy advocacyLegislaturePopulationHealth care reformPolitical scienceNursingPublic relationsEnvironmental healthLaw

Abstract

fetched live from OpenAlex

The societal responsibility of physicians to be health advocates, both at the population and patient level is necessary to positively influence public health and policy. Physicians must commit to learn about policy reform and the legislative process. Several regulatory physician organizations emphasize the importance of health. In addition, the Association of American Medical Colleges' (AAMC) Medical Schools Objectives Project, the Medical Council of Canada Qualifying Examination objectives and several Canadian medical schools outline advocacy as an objective. As a result, several US medical schools have designed and incorporated health advocacy into their curricula. Canadian medical schools, however, have been lagging behind. To address this deficiency, the University of Alberta and the University of Calgary hosted the 1st Annual Alberta Political Action Day (PAD) to engage medical students in advocacy and the policy making process. The two-day time requirement of PAD makes it an efficient model to incorporate health advocacy into the already demanding undergraduate medical curriculum. Canadian medical schools must follow the American example and further integrate initiatives such as PAD to teach health advocacy. The skills developed will enhance student's comprehension of how they can shape health policy and truly advocate for optimal patient care.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
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 integrity0.0000.002
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 head0.033
GPT teacher head0.357
Teacher spread0.324 · 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

Citations11
Published2010
Admission routes3
Has abstractyes

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