Perspectives on ART adherence among Zambian adults living with HIV: insights raised using HIV-related disability frameworks
Bibliographic record
Abstract
Anti-retroviral treatment (ART) has improved the survival of people living with HIV in Africa. Living with chronic HIV comes with new health and functional challenges and the need to manage ART adherence. The Sepo Study applied disability frameworks to better understand living with chronic HIV while using ART. The study followed 35 people (18 women, 17 men) living with HIV and on ART 6 months or longer in private and public health facilities in Lusaka, Zambia over 18-months (2012-2015). A total of 99 in-depth interviews were conducted. Conventional content analysis and NVIVOv10 were applied to analyse the data. Participants were adhering to ART at the times of the interviews and therefore less likely to report major challenges with adherence. Three main themes emerged from the data related to adherence. Firstly, ART was regarded as "giving life", which underscored adherence. Secondly, all participants described strategies for to managehealth and functional limitations, which they attributed as side-effects or chronicity. Thirdly, participants described experiences of uncertainty, including the efficacy of new regimens, potential loss of functioning, risk of new health problems, and death. Long-term ART managment in Africa needs to integrate rehabilitation approaches to address functional limitations, uncertainties, strengthen and support for adherence.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".