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Record W4396562329 · doi:10.1136/bmjoq-2023-002677

Practical guide to implementing patient-reported outcome measures in gender-affirming care: evaluating acceptability, appropriateness and feasibility

2024· article· en· W4396562329 on OpenAlexaff
Rakhshan Kamran, Liam Jackman, Anna Laws, Melissa Stepney, Conrad Harrison, Abhilash Jain, Jeremy Rodrigues

Bibliographic record

VenueBMJ Open Quality · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
FundersNational Institute for Health and Care Research
KeywordsPromHealth careIntervention (counseling)MedicineTransgenderPatient-reported outcomeHealth professionalsFamily medicinePsychologyPhysical therapyClinical psychologyNursingQuality of life (healthcare)Obstetrics

Abstract

fetched live from OpenAlex

OBJECTIVE: Assess acceptability, appropriateness and feasibility of the Practical Guide to Implementing patient-reported outcome measures (PROMs) in Gender-Affirming Care (PG-PROM-GAC) from a sample of patients and healthcare professionals. DESIGN: Cross-sectional study conducted August-October 2023. SETTING: Participants were recruited from a National Health Service (NHS) gender clinic. PARTICIPANTS: Patient participants seeking care and healthcare professionals working at an NHS gender clinic were eligible for participation. The PG-PROM-GAC was sent to participants via email for review. OUTCOME MEASURES: Three validated tools to measure acceptability, appropriateness and feasibility were administered: the acceptability of intervention measure (AIM), intervention appropriateness measure (IAM) and feasibility of intervention measure (FIM). The percentage of participants indicating agreement or disagreement with items on the AIM, IAM and FIM was calculated. RESULTS: A total of 132 transgender and gender diverse (TGD) patients (mean age, SD: 33, 14) and 13 gender-affirming healthcare professionals (mean age, SD: 43, 11) completed the AIM, IAM and FIM, representing a range of gender identities. The cumulative percentage of patients indicating agree or strongly agree on the AIM, IAM and FIM for the patient-relevant strategies in the PG-PROM-GAC was over 50% for each item. The cumulative percentage of patients indicating disagree or strongly disagree on the AIM, IAM and FIM for the PG-PROM-GAC was less than 20% for each item. The cumulative percentage of healthcare professionals indicating agree or strongly agree on the AIM, IAM and FIM for the healthcare professional-relevant strategies in the PG-PROM-GAC was over 38% for each item. The cumulative percentage of healthcare professionals indicating disagree or strongly disagree on the AIM, IAM and FIM for the PG-PROM-GAC was less than 15% for each item. CONCLUSIONS: Gender-affirming healthcare professionals and TGD patients find the PG-PROM-GAC acceptable, appropriate and feasible. The PG-PROM-GAC is ready-to-use for clinicians, policy-makers and researchers committed to service improvement for gender-affirming care.

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)0.194
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.194
Threshold uncertainty score0.994

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1940.180
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.003
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.004

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 head0.622
GPT teacher head0.662
Teacher spread0.040 · 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.

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

Citations11
Published2024
Admission routes1
Has abstractyes

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