Discriminatory Attitudes Towards People Living With HIV Among Key Populations in Nigeria: A Latent Class Analysis
Bibliographic record
Abstract
Background: HIV‐related discrimination remains a significant barrier to the uptake of HIV prevention and treatment services in sub‐Saharan Africa among key populations (KPs). However, despite the substantial risk of HIV among peers within their social networks, there is a paucity of data on their attitudes towards people living with HIV (PLHIV). This study aimed to examine discriminatory attitudes towards PLHIV among KPs in Nigeria. Methods: This study was a secondary data analysis of the 2020 Integrated Biological and Behavioral Surveillance Survey in Nigeria, which included 17,975 KPs. We operationalized discriminatory attitudes as negative responses to questions on caring for PLHIV, buying food from PLHIV, working with PLHIV, sharing a meal with PLHIV, and a positive response to quarantining PLHIV. We conducted weighted descriptive statistics to summarize the data, and latent class analysis was used to determine the patterns of discriminatory attitudes. The predicted probabilities of the classes for each KP characteristic were estimated while holding all other characteristics in the model at their means. The data analysis was conducted using Stata 18. Results: About 13.5% of participants indicated they would not provide care for PLHIV , 29.7% would not buy food from them, 15.8% would not work with them, 27.9% would not share a meal with them,and 16.3% believed that PLHIV should be quarantined. Three latent classes of discriminatory attitudes were identified: low discriminatory attitude (65%), moderate discriminatory attitude (23%), and high discriminatory attitude (11%). The highest predicted probability of high discriminatory attitude was observed among KPs with unknown HIV status (42%), followed by those residing in the Southeast region (39.7%). Conclusion: Discriminatory attitudes towards PLHIV are common among KPs in Nigeria. Interventions aimed at reducing HIV‐related discrimination should also target KPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".