Impact evaluation of the TAMANI project to improve maternal and child health in Tanzania
Bibliographic record
Abstract
BACKGROUND: The Tabora Maternal and Newborn Health Initiative project was a multicomponent intervention to improve maternal and newborn health in the Tabora region of Tanzania. Components included training healthcare providers and community health workers, infrastructure upgrades, and improvements to health management. This study aimed to examine the impact of trainings on four key outcomes: skilled birth attendance, antenatal care, respectful maternity care and patient-provider communication. METHODS: Trainings were delivered sequentially at four time points between 2018 and 2019 in eight districts (two districts at a time). Cross-sectional surveys were administered to a random sample of households in all districts at baseline and after each training wave. Due to practical necessities, the original stepped wedge cluster randomised design of the evaluation was altered mid-programme. Therefore, a difference-in-differences for multiple groups in multiple periods was adopted to compare outcomes in treated districts to not yet treated districts. Risk differences were estimated for the overall average treatment effect on the treated and group/time dynamic effects. RESULTS: Respondents reported 3895 deliveries over the course of the study. The intervention was associated with a 12.9 percentage point increase in skilled birth attendance (95% CI 0.4 to 25.4), which began to increase 4 months after the end of training in each district. There was little evidence of impact on antenatal care visits, respectful treatment during delivery and patient-provider communication. CONCLUSION: Interventions to train local healthcare workers in basic and comprehensive emergency obstetric and newborn care increased skilled birth attendance but had limited impact on other pregnancy-related outcomes.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".