Challenges in Integrating LGBTQ+ Inclusivity into Hong Kong’s Mental Health Care:Barriers to Effective Psychiatric Services
Bibliographic record
Abstract
This thesis examines the challenges of integrating LGBTQ+ inclusivity into HongKong’s mental health care system, focusing on barriers to effective psychiatric services forlesbian, gay, bisexual, transgender, and queer individuals. Guided by Queer Theory andIntersectionality, the study addresses two research questions: how the current system meetsLGBTQ+ needs and the primary challenges faced by providers in delivering inclusive care.Through semi-structured interviews with two service users and one psychiatric nurse, thematicanalysis identifies four barriers: lack of cultural competence training, heteronormative normsand cultural stigma, lack of policy implementation, and resource constraints. Policy analysis ofHong Kong’s key documents, including the Mental Health Review Report (2017) and HospitalAuthority Guidelines (2019), reveals absent provisions for LGBTQ+-specific stressors, suchas stigma and family rejection. International policy references from Taiwan, Singapore,Australia, the UK, and Canada contextualize these gaps, highlighting inclusive practices likemandated training. Findings indicate that Hong Kong’s system fails to provide affirming care,exacerbating mental health disparities, while providers face inadequate training, cultural biases,legal ambiguities, and resource shortages. The study contributes theoretically by applyingQueer Theory and Intersectionality to Hong Kong’s context, empirically by filling gaps in livedexperiences and provider perspectives, and practically by informing inclusive policy reforms.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".