Barriers and facilitators for accessing and prescribing hormone therapy in primary care for transgender adults: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to understand the extent and type of evidence in relation to barriers and facilitators experienced by transgender adults in accessing hormone therapy. It will also explore the experiences of primary care practitioners in prescribing hormone therapy in primary care. INTRODUCTION: Providing care to transgender patients is a rapidly growing area of primary care. Despite the existence of clinical practice guidelines that support the prescription of gender-affirming hormone therapy in primary care, only a small number of primary care providers are offering this care. This review will seek to advance research on this topic by examining the barriers and facilitators of hormone prescription for transgender adults in primary care. INCLUSION CRITERIA: This review will consider research on primary care practitioners who prescribe hormone therapy to transgender adults. It will also focus on transgender adults who seek hormone therapy in primary care. Only studies that examine barriers and facilitators in primary care will be included. The review will include qualitative, quantitative, and mixed methods studies, in addition to systematic reviews and meta-analyses. METHODS: The search will include MEDLINE, CINAHL, EmCare, and Nursing and Allied Health Premium. No date limits will be applied to the search. Only articles written in English will be eligible for inclusion. Articles will be reviewed and data extracted by 2 independent reviewers. The results of the extracted data will be presented in a narrative summary with accompanying tables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.098 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".