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Record W3210054079 · doi:10.1089/trgh.2020.0181

Assessment of Knowledge, Comfort, and Skills Working with Transgender Clients of Saskatchewan Family Physicians, Family Medicine Residents, and Nurse Practitioners

2021· article· en· W3210054079 on OpenAlexaffabout
Lisette Christopherson, K. McLaren, Lori Schramm, Carla Holinaty, Ginger Ruddy, Emily Boughner, Adam Clay, Michelle McCarron, Megan Clark

Bibliographic record

VenueTransgender Health · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsSaskatchewan Health AuthoritySaskatchewan HealthUniversity of Saskatchewan
Fundersnot available
KeywordsTransgenderFamily medicineNurse practitionersNursingMedicineHealth carePsychology

Abstract

fetched live from OpenAlex

People who are transgender and gender diverse (TGD) report suboptimal care from health care providers. A cross-sectional survey was codesigned with community partners to assess the knowledge, comfort, and skills of family physicians, family medicine residents, and nurse practitioners working with TGD patients in Saskatchewan. It was administered from August to October 2019. Of 188 participants, 30% and 96% were comfortable providing transition-related and non-transition-related medical care to patients who are TGD, respectively. Interest in further training in providing transition-related medical care and cultural safety was high. No significant differences between provider groups were observed. Based on our results, provincial training initiatives will be undertaken.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.062
GPT teacher head0.407
Teacher spread0.345 · 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 designObservational
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

Citations7
Published2021
Admission routes2
Has abstractyes

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