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Medical student exposure to women’s health concepts and practices: a content analysis of curriculum at Canadian medical schools

2021· other· en· W6959195860 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSummative assessmentCurriculumContent analysisQuality (philosophy)Affect (linguistics)Medical school

Abstract

fetched live from OpenAlex

Abstract Background Women’s health (WH) includes a broad array of concerns and challenges that affect health across the lifespan. Considerable research shows that women continue to experience disparities in access to and quality of care. Apart from surveys of medical trainees and faculty, little research and none in Canada examined medical curriculum for WH. This study assessed how Canadian medical schools integrate WH in their curriculum. Methods We used deductive and summative content analysis to describe instances and the nature of WH topics in program and course descriptions that were publicly-available on web sites of Canadian medical schools. We reported results using summary statistics and text examples. We employed a framework, tested in our prior research, that included mention of women’s health principles and practices relevant to any health concern or condition including factors (e.g. sex, gender, social determinants) that influence health, and access to or quality of care. Results We retrieved 1459 documents from 16 medical schools (median 49.5, range 16 to 301). Few mentioned WH (125, 8.6 %), and the quantity of mentions varied by school (range 0.0–37.5 %). Pre-clerkship course documents more frequently mentioned WH (61/374, 17.3 %, chi square 43.2, p

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)0.000
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.6260.000

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 head0.057
GPT teacher head0.331
Teacher spread0.274 · 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
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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