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Record W4394685113 · doi:10.1016/j.jisako.2024.04.006

How do we treat our male and female patients? – A primer on gender-based health care inequities

2024· review· en· W4394685113 on OpenAlexaff
María Loreto Figueroa, Laurie A. Hiemstra

Bibliographic record

VenueJournal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine · 2024
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsBanff CentreUniversity of Calgary
Fundersnot available
KeywordsPrimer (cosmetics)MedicineHealth careDemographyFamily medicinePsychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Health is a fundamental human right, yet disparities in healthcare, based on gender, persist for women. These inequities stem from a patriarchal society that has regarded men as the default standard, leading to women being treated merely as smaller men. Contributing to these disparities are the gender stereotypes that pervade our society. Women possess differences in anatomy, physiology, psychology and social experience than men. To achieve health equity, it is vital to understand and be open to consider and evaluate these aspects in each individual patient. This requires an understanding of our own biases and a commitment to valuing diversity in both patient and caregiver. Improving equity and diversity throughout all aspects of the medical system will be necessary to provide optimal patient care for all.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.341
Teacher spread0.282 · 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 designOther design
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes1
Has abstractyes

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