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Record W4396767956 · doi:10.1097/og9.0000000000000003

A Gender-Affirming Approach to Contraceptive Care for Transgender and Gender-Diverse Patients

2024· article· en· W4396767956 on OpenAlexaff
Dustin Costescu, Carys Massarella, William J. Powers, Sukhbir S. Singh

Bibliographic record

VenueO&G Open · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsHamilton General Hospital
FundersBayer
KeywordsTransgenderTransgender womenTransgender PersonTransgender peopleGender studiesPsychologyMedicineSociologyFamily medicineHuman immunodeficiency virus (HIV)Men who have sex with men

Abstract

fetched live from OpenAlex

Transgender and gender-diverse (TGD) reproductive health care is a field with an abundance of misinformation and a paucity of quality literature available for both health care professionals and their patients. Clinicians often receive limited education and training in this area, and TGD individuals face many barriers to reproductive health care, including the lack of gender-affirming, inclusive, and knowledgeable clinicians as well as concerns about gender biases. As such, TGD individuals often feel uncomfortable discussing their reproductive health with their health care professionals and are deterred from seeking the appropriate care they need. Contraceptive counseling is a key component of reproductive health care but is often neglected in discussions between TGD patients and their health care professionals. Clinicians must strive to meet the contraceptive health care needs and desires of TGD patients in a gender-affirming manner within a safe and accepting space. Here, we summarize the current reproductive health care landscape and provide contemporary perspectives on how to improve contraceptive care for TGD individuals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0050.004
Open science0.0010.011
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0110.001

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 head0.169
GPT teacher head0.432
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2024
Admission routes1
Has abstractyes

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