Patient and healthcare professional perspectives on implementing patient-reported outcome measures in gender-affirming care: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: Patient and healthcare professional perspectives are needed to develop a gender-affirming care patient-reported outcome measure (PROM) implementation plan. We aimed to identify top considerations relevant to gender-affirming care PROM implementation from patient and healthcare professional perspectives. DESIGN, SETTINGS AND PARTICIPANTS: This qualitative study conducted in the UK between January and April 2023 includes focus groups with a patient sample diverse in age and gender identity, and a healthcare professional sample diverse in age and role. Established methods in implementation science and the Consolidated Framework for Implementation Research were used to create interview guides, and analyse data. Focus groups were audio recorded, transcribed verbatim and analysed by two independent researchers. Patient and healthcare professional focus groups were conducted separately. PRIMARY OUTCOME MEASURES: Patient and healthcare professional perspectives on PROM implementation were explored through focus groups and until data saturation. RESULTS: A total of 7 virtual focus groups were conducted with 24 participants (14 patients, mean (SD) age, 43 (14.5); 10 healthcare professionals, mean (SD) age, 46 (11.3)). From patient perspectives, key barriers to PROM implementation were mistrust with PROMs, lack of accessibility, burden, and lack of communication on why PROMs are important and how they will help care. From healthcare professional perspectives, key barriers to PROM implementation were lack of accessibility, burden with PROM administration and scoring, costs of implementation (financial and time), and lack of communication on what PROMs are and how they benefit service provision. CONCLUSION: Gender-affirming care PROM implementation must address: patient mistrust with PROMs, accessibility, communication on what PROMs are and how they can be used, reducing burden, and hybridised implementation. These factors may also be applicable to other clinical areas interested in implementing PROMs.
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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 | 0.055 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".