Developing feasible and acceptable strategies for integrating the use of patient-reported outcome measures (PROMs) in gender-affirming care: An implementation study
Bibliographic record
Abstract
OBJECTIVE: Use CFIR guidance to create comprehensive, evidence-based, feasible, and acceptable gender-affirming care PROM implementation strategies. DESIGN, SETTING, PARTICIPANTS: A 3-Phase participatory process was followed to design feasible and acceptable strategies for integrating PROMs in gender-affirming care. In Phase 1, barriers and enablers to PROM implementation for gender-affirming care were identified from a previous systematic review and our prior qualitative study. We used the CFIR-ERIC tool to match previously identified barriers and enablers with expert-endorsed implementation strategies. In Phase 2, implementation strategy outputs from CFIR-ERIC were organised according to cumulative percentage value. In Phase 3, gender-affirming care PROM implementation strategies underwent iterative refinement based on rounds of stakeholder feedback with seven patient and public partners and a gender-affirming healthcare professional. RESULTS: The systematic review and qualitative study identified barriers and enablers to PROM implementation spanning all five CFIR domains, and 30 CFIR constructs. The top healthcare professional-relevant strategies to PROM implementation from the CFIR-ERIC output include: identifying and preparing implementation champions, collecting feedback on PROM implementation, and capturing and sharing local knowledge between clinics on implementation. Top patient-relevant strategies include: having educational material on PROMs, ensuring adaptability of PROMs, and collaborating with key local organisations who may be able to support patients. CONCLUSIONS: This study developed evidence-based, feasible, and acceptable strategies for integrating PROMs in gender-affirming care, representing evidence from a systematic review of 286 international articles, a qualitative study of 24 gender-affirming care patients and healthcare professionals, and iteration from 7 patient and public partners and a gender-affirming healthcare professional. The finalised strategies include patient- and healthcare professional-relevant strategies for implementing PROMs in gender-affirming care. Clinicians and researchers can select and tailor implementation strategies best applying to their gender-affirming care setting.
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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 | 0.468 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".