Twelve-month Treatment Retention and Associated Factors: A Comparison of 2 Medically Assisted Therapy Clinics in Dar es Salaam, Tanzania
Bibliographic record
Abstract
OBJECTIVES: Retention in methadone maintenance treatment is instrumental in achieving better treatment outcomes. In this study, we compared 2 medication-assisted treatment (MAT) clinics in Dar es Salaam, Tanzania with respect to patient characteristics, outcomes, and factors that predict 12-month treatment retention. METHODS: This retrospective registry-based cohort study utilized data collected for routine clinical and program monitoring at 2 sites, Mwananyamala and Muhimbili MAT clinics. Cumulative retention in treatment was calculated using life tables. The analysis of treatment retention predictor variables used both Kaplan-Meier and Cox proportional hazard analyses. RESULTS: We examined the socio-demographic and program-related characteristics of 362 (181 from each clinic) patients. Twelve-month treatment retention was higher at Mwananyamala (73%) than Muhimbili (64%) MAT clinic, but the difference was not significant. In both clinics, a higher methadone dose (>60mg) significantly predicted treatment retention ( P < 0.05). Being employed and traveling an average short distance (<5 km) from home to clinic significantly increased the likelihood of remaining in treatment in Muhimbili MAT clinic (P< 0.05) only. CONCLUSIONS: A methadone dose of 60 mg and above was associated with longer retention in treatment. At 1 clinic in a denser and more central location, employment and a short travel distance from home to clinic were associated with longer tenure in treatment. These findings have potential implications for clinical practice, research, and scaling up MAT services in Tanzania.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".