Stigmatization, Medication Adherence and Resilience Among Recently Diagnosed People Living With HIV/AIDS (PLWHA): A Mixed‐Method Study
Bibliographic record
Abstract
Aim and Objectives: To investigate the level of stigma, medication adherence and resilience among recently diagnosed people living with HIV/AIDS (PLWHA) and explore the relationship between medication adherence, stigmatization and resilience. Design/Method: This is a convergent-parallel mixed-method design involving both qualitative and quantitative research methodologies. The quantitative aspect utilized a cross-sectional design among 200 PLWHA at the anti-retroviral therapy clinic of the Lagos University Teaching Hospital, Lagos, Nigeria, whereas the qualitative part entailed semi-structured, in-depth interviews of 26 PLWHA. Spearman's rho correlation was used to explore the relationship between medication adherence, stigmatization and resilience, and qualitative data were analysed using thematic analysis. Result: Four themes emerged from the qualitative analysis, including building resilience, experiences relating to diagnosis, experiences related to treatment and factors facilitating medication adherence. Overall, 113 (57%) experienced a high level of stigma, 149 (76%) reported high medication adherence, and above average 115 (57.2%) demonstrated high resilience. Conclusion: In this study, PLWHA in Nigeria who recently received their diagnosis experienced a high level of stigma, resilience and medication adherence. However, nearly one-third of the participants were non-adherent to medication due to several reasons. This noteworthy proportion of non-adherence needs to be addressed while improving resilience and reducing stigmatization.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".