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Record W1999507833 · doi:10.3109/0142159x.2013.770453

Laying the foundation: Teaching policy and advocacy to medical trainees

2013· article· en· W1999507833 on OpenAlexaffabout
Danielle Martin, Susan Hum, Cynthia Whitehead

Bibliographic record

VenueMedical Teacher · 2013
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsPrincess Margaret Cancer CentreWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsCurriculumMedical educationHealth careMedicineFoundation (evidence)Test (biology)NursingPsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: A novel and comprehensive two-year health policy curriculum was developed and implemented for family medicine residents at two University of Toronto-affiliated teaching sites. AIM: To evaluate the impact of the curriculum on residents' knowledge of health policy issues, and its usefulness to their learning. METHOD: The evaluation included a pre-post delivery assessment of residents' content-based knowledge of issues in the Canadian healthcare system. Residents were also asked to evaluate the content, process and usefulness of the health policy curriculum. RESULTS: At the end, more than two-thirds of residents had a better understanding of the Canadian healthcare system. The overall pre-post scores showed that residents retained content-based facts in some detail. However, more importantly, residents' positive evaluations of the curriculum indicated they were engaged, enthusiastic and recognized its importance for their learning. CONCLUSION: Despite residents' positive evaluations, questions remain as to how best to assess the success of health policy curricula. Moving beyond the popular pre-post test, less traditional approaches might complement standard program evaluation methods in future. As educators increasingly develop curricula aimed at non-biomedical expertise, we must consider how we can most meaningfully evaluate long-term impact on graduates' approach to clinical practice and their engagement in health system advocacy.

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)0.018
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 score0.782
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.018
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.032
GPT teacher head0.363
Teacher spread0.330 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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