LGBTIQA+ Patient Liaison Service—Seeking to Improve the Experience of Hospitalised LGBTIQA+ People
Bibliographic record
Abstract
RATIONALE: People in LGBTIQA+ communities often have high unmet health care needs partially due to experiences of poor treatment and LGBTIQA+ identity-based discrimination in health care settings. LGBTIQA+ informed health care providers are essential to enhancing care. In 2021, The Royal Melbourne Hospital established an LGBTIQA+ Liaison Service. This service consisted of lived experience practitioners with allied health backgrounds; they supported patients and delivered education to staff. AIMS: This study aimed to evaluate the feasibility (demand, practicality and limited efficacy) of an LGBTIQA+ Liaison Service at a large tertiary hospital in Australia. METHODS: This prospective feasibility study was conducted at a large tertiary hospital in Victoria, Australia. Patient demographics (demand) and services provided (practicality) were documented. Self-rated staff understanding of LGBTIQA+ care in hospital was collected pre and post-education sessions (limited efficacy). RESULTS: In 2022, 63 referrals were made to the LGBTIQA+ Liaison Service from 44 patients (median age 27 [26-40 years]). Most were transgender and gender diverse people (n = 34, 54%) and one quarter were First Nations people (n = 16, 25%). The most common interventions were liaising with internal and external parties-including multidisciplinary teams-and providing peer support. Staff (n = 1226) who attended LGBTIQA+ education sessions reported statistical improvements in understanding the experience of LGBTIQA+ people, inclusive practice, available supports and referring to the Service. CONCLUSION: Within a large tertiary hospital, there was demand for the LGBTIQA+ Liaison Service from both staff and patients, particularly transgender, gender diverse and First Nations peoples. The education and support delivered and provided in this setting improved clinician capability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".