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Record W2530090510 · doi:10.1186/s13293-016-0093-7

Integrating topics of sex and gender into medical curricula—lessons from the international community

2016· editorial· en· W2530090510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiology of Sex Differences · 2016
Typeeditorial
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsCurriculumMedical educationHealth careMedicineAlternative medicineOrder (exchange)Engineering ethicsPsychologyPolitical sciencePathologyPedagogyEngineering

Abstract

fetched live from OpenAlex

In the era of individualized medicine, training future scientists and health-care providers in the principles of sex- and gender-based differences in health and disease is critical in order to optimize patient care. International successes to incorporate these concepts into medical curricula can provide a template for others to follow. Methodologies and resources are provided that can be adopted and adapted to specific needs of other institutions and learning situations.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.090
GPT teacher head0.407
Teacher spread0.316 · 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