Social Work and Sexual Minorities: The Health and Psychosocial Realities of Queer Men in Nigeria and Its Implication for Social Work Practice
Bibliographic record
Abstract
BACKGROUND: Nigeria is a nation characterized by diverse cultures, religions, and policies that often foster discrimination, oppression, and violence against sexual minorities. This hostile environment can significantly impact social work practices involving these groups. Consequently, this study aimed to investigate the health and psychosocial realities of queer men in Nigeria and their implications for social work practice. METHODS: A qualitative phenomenological approach was adopted for the study. Data were collected from 28 participants, including 16 queer men and 12 social workers, and analyzed thematically. RESULTS: The findings revealed experiences of discrimination and insecurity, which contribute to both physical and psychological health issues. Additionally, the study highlighted a lack of understanding and acceptance among social workers regarding practices involving sexual minorities in Nigeria. DISCOURSE: The results highlight how queer individuals may experience limited access to healthcare and receive limited support from social workers in advancing their healthcare needs. CONCLUSION: It is recommended that social work education incorporate discussions around working with sexual minorities, as a better understanding of this population will enhance acceptance, improve practice, and encourage advocacy for reform in discriminatory policies and practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".