Are we asking the right questions? Working with the LGBTQ+ community to prioritise healthcare research themes
Bibliographic record
Abstract
BACKGROUND: Conversations about research priorities with members of the public are rarely designed specifically to include people who identify as Lesbian, Gay, Bisexual, Transgender and Queer (LGBTQ+) and are not researchers. METHODS: Generally, to address this gap, and specifically, to inform future research for CLS, we carried out a rapid review of published research priority sets covering LGBTQ+ topics, and an online workshop to prioritise identified themes. RESULTS: Rapid review: results. The rapid review identified 18 LGBTQ+ research priority sets. Some focussed on specific populations such as women or men, younger or older people or people living within families. Five addressed transgender and gender non- conforming populations. All of the research priority sets originated from English-speaking, high and middle-income countries (UK, US, Canada, and Australia), and date from 2016 onwards. Prioritization approaches were wide-ranging from personal commentary to expert workshops and surveys. Participants involved in setting priorities mostly included research academics, health practitioners and advocacy organisations, two studies involved LGBTQ+ public in their process. Research priorities identified in this review were then grouped into themes which were prioritised during the workshop. Workshop: results. For the workshop, participants were recruited using local (Cambridge, UK) LGBTQ+ networks and a national advert to a public involvement in research matching website to take part in an online discussion workshop. Those that took part were offered payment for their time in preparing for the workshop and taking part. Participants personal priorities and experiences contributed to a consensus development process and a final ranked list of seven research themes and participants' experiences of healthcare, mental health advocacy, care homes, caring responsibilities, schools and family units added additional context. CONCLUSIONS: From the workshop the three research themes prioritised were: healthcare services delivery, prevention, and particular challenges / intersectionality of multiple challenges for people identifying as LGBTQ+. Research themes interconnected in many ways and this was demonstrated by the comments from workshop participants. This paper offers insights into why these priorities were important from participants' perspectives and detail about how to run an inclusive and respectful public involvement research exercise. On a practical level these themes will directly inform future research direction for CLS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".