Mak Nyahs in Malaysia: Does HIV/AIDS Knowledge Really Reduce HIV-Related Risk Behaviours?
Bibliographic record
Abstract
Introduction. Male-to-female transgender individuals are known as mak nyahs in Malaysia. Akin to many other countries, mak nyahs face discriminations in securing proper employment, thus many end up in sex trade, exposing them to HIV-related high risk behaviours. This paper aims to investigate the knowledge, attitude, and risk behaviours in HIV/AIDS among mak nyahs in Malaysia. Method. Validated questionnaires in local language were used. Knowledge, attitude, and HIV-related risk behaviours were measured. With respondent-driven sampling method, 54 subjects participated in the survey. IBM SPSS was used for data analysis. Results. Majority of the subjects reported to have HIV-related sexual risk, although only a quarter were sex workers. Misconceptions still remained among the subjects. Inconsistent condom use was common. Adequate knowledge did result in better attitude towards HIV/AIDS. However, multiple regression analysis showed that subjects with higher knowledge in HIV/AIDS tend to have higher sexual risks (β = 0.320, p = 0.030). Conclusion. Educating the transgender community about HIV/AIDS does not seem to adequately reduce the HIV-related sexual risks. Other approaches, i.e. socio-cultural values and religious practices may be effective to instil higher levels of awareness and self-responsibility in this community to eliminate HIVrelated high risk behaviours.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".