Feasibility, challenges, and lessons learned in implementing antiretroviral adherence club models in Northern Tanzania
Bibliographic record
Abstract
Introduction: In Tanzania, nearly 1.6 million adults were living with HIV in 2022, with an HIV prevalence rate of 4.4%. We piloted community health worker-managed adherence clubs in two clinics in Shinyanga, Tanzania. We previously demonstrated better stability in care and improved treatment adherence following the establishment of clubs. This study evaluated referral back to the facility, feasibility, and challenges to inform potential scale-up.Method: The study included clubs from two clinics between July 2018 and September 2019, Ngokolo (13 clubs) and Bugisi (33 clubs), all located in the Shinyanga region. Clinicians invited stable clients to join an AC if they met eligibility criteria. Logistic regression models identified factors associated with the likelihood of clients’ referral back to the facilitiesResults: The setup of the adherence clubs was feasible and successful. In one year, 46 clubs with 617 clients were established. Thirty-eight clients (6.3 %) were referred back to the clinics. Factors associated with being referred back were lower age (below 25 compared to above), having ever missed AC visits, and a rural site. Challenges included start-up problems with wrongful enrolments, the need for additional staff to facilitate the club model, the duplication of data collection tools, and the need for youth-friendly adherence clubs.Conclusion: Task shifting to community health workers to manage the clubs was successful. Despite its success, it required a higher level of organisation, particularly during the setup phase. These findings support the revision of the National HIV guideline to formally integrate community health worker-led adherence clubs, ensuring scalability and sustainability
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".