Advancing Access to Quality LGBTQIA+ Health Care: Gender Discrimination, Socio-Cultural, and Mental Health Issues: A Mixed-Method Study
Bibliographic record
Abstract
Recent research highlights the lack of knowledge and reduced skills of health care professionals in communicating with people from the LGBTQIA+ community. This often occurs due to reduced continuing education on social issues in the health sector. The purpose of this research was to study the readiness of health care professionals to manage the social and mental health issues of the LGBTQIA+ community. In particular, the cultural competence of health care professionals targeted at gender identity, the recognition of the level of mastery of soft skills, and the relevant experiences of the participants were studied. For the purposes of conducting this research, a mixed methodology was used to pursue an in-depth study of human beliefs, attitudes, perceptions, ideas, and experiences. More specifically, a previously validated research tool was used to measure cultural competence and assess soft skills. At the same time, interviews were conducted with health care professionals for a more complete understanding of their skills and attitudes. The study comprised a quantitative study involving 479 health care professionals and a qualitative study involving 20 health care professionals, with results from each study. The results showed that the health care professionals' knowledge of the LGBTQIA+ community is sufficient, but their skills and attitudes towards gender diversity are limited. In addition, the level of acquisition of soft skills by health care professionals is low, and there is insufficient training for health care professionals with regards to social issues. In conclusion, a targeted and structured educational intervention for health care professionals is required to avoid future unfortunate behaviours, and to ensure that the health care provided to healthy and sick populations, regardless of sexual orientation, is adequate.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".