Development of expertise in mental health service provision for lesbian, gay, bisexual and transgender communities
Bibliographic record
Abstract
OBJECTIVES: There are significant health disparities according to sexual orientation and gender identity, particularly in mental health; however, very few mental health professionals specialise in caring for lesbian, gay, bisexual and transgender (LGBT) communities. The purpose of this study was to explore how providers with LGBT-focused practices have developed their capacity for working with these populations. METHODS: Eight semi-structured interviews were conducted with practising mental health service providers with extensive experience serving LGBT individuals. Participants represented four professional disciplines: psychiatry (n = 2); social work (n = 3); psychotherapy (n = 2), and psychology (n = 1). The data were analysed for themes that were identified using a descriptive phenomenological approach. RESULTS: All providers self-identified as members of LGBT communities; however, most agreed that this membership was not necessary to provide supportive, appropriate care for LGBT individuals. Providers described their self-identity as members of LGBT communities, associated lived experiences and recognition of the need for mental health services that are sensitive to the unique needs of LGBT individuals as influential factors in their career decisions. The lack of training opportunities and resources specific to the provision of LGBT-sensitive mental health services was highlighted. Provider recommendations included the introduction of mandatory LGBT health content in education curricula that addresses basic LGBT-related terminology, appropriate interview questions to facilitate the disclosure of sexual orientation and gender identity, information regarding the health impact of heterosexism and homophobia, and specific health care needs of sexual and gender identity minority people. CONCLUSIONS: Data from this study suggest there are few opportunities for medical providers to access training and gain expertise in the provision of care to LGBT people. Additional research is needed to consider whether the lack of LGBT health content in medical and psychiatric training programme curricula indirectly contributes to the health disparities experienced by these populations.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".