Medication assisted therapy (MAT) and substance use disorders in Tanzania
Bibliographic record
Abstract
Purpose Substance use is among the risk factors associated with both HIV/AIDS and non communicable diseases (NCDs). The aim of this paper is to describe the development of the medication assisted therapy (MAT) in the treatment of substance use disorders and opportunities for further interventions in Tanzania. Design/methodology/approach A review of MAT pilot project documentation, existing published and grey literature on substance misuse in Tanzania was used to describe the scope of this paper. MAT as a program focuses on the treatment of opiod dependent individuals using methadone in a national hospital in Tanzania. It is delivered by a team of trained interprofessionals coordinating with community partners. Findings The findings indicate an uptake of pharmacotherapy in the treatment of substance use disorders as an adjunct to traditional counseling approaches in low resource settings. Program acceptability and reach within a short period of time by the opiod dependent individuals is shown. Practical implications National buy‐in is critical for developments of new interventions. Given adequate resources, it is feasible to integrate MAT for the treatment of substance use disorders within health care systems in poor resource settings. To ensure the success of the program, sustainable efforts and scaling up to include alcohol and tobacco dependence treatment is crucial. The local capacity building is required including a need for designing appropriate policies to address alcohol and tobacco use in Tanzania. Originality/value The intervention is the only one in sub‐Saharan Africa. MAT may serve as a practice model for other countries in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".