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I-CARE : une formation pionnière par apprentissage en ligne sur la santé des personnes LGBTIQ+

2023· article· fr· W4382345111 on OpenAlexaff
Raphaël Bize, Erika Volkmar, Zoé Blanc-Scuderi, Denise Médico, Adèle Zufferey, Camille Béziane, Arnaud Merglen, Céline Brockmann, Patrick Bodenmann

Bibliographic record

VenueRevue Médicale Suisse · 2023
Typearticle
Languagefr
FieldHealth Professions
TopicHealth and Medical Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceTransgenderSociologyArtGender studies

Abstract

fetched live from OpenAlex

While several recent studies suggest that approximately 1 in 6 young people in Switzerland are part of the rainbow diversity, a high proportion of health professionals have never had a course on LGBTIQ+ (lesbian, gay, bisexual, transgender, intersex, queer, questioning or other) health. This situation leads to significant gaps in the medical care of LGBTIQ+ persons as well as difficulties in accessing equitable, culturally appropriate and quality care. This article presents the ambitious and novel e-learning project I-CARE (Improving Care and Access for Rainbow Equity) which should contribute, from the end of this year, to filling the current gaps in the undergraduate and continuing education of health professionals.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.005

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 head0.041
GPT teacher head0.390
Teacher spread0.349 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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