The effects of community-based interventions on the uptake of selected maternal and child health services: experiences of the IMCHA project in Iringa Tanzania, 2015‐2020
Bibliographic record
Abstract
BACKGROUND: Maternal and child health (MCH) improvement has been prioritised in resource-constrained countries. This is due to the desire to meet the global sustainable development goals of achieving a maternal mortality rate of 70 per 100000 live births by 2030. The uptake of key maternal and child health services is crucial for reducing maternal and child health mortalities. Community-Based Interventions (CBIs) have been regarded as among the important strategies to improve maternal and child health service uptake. However, a paucity of studies examines the impacts of CBIs and related strategies on maternal and child health. This paper unveils the contribution of CBIs toward improving MCH in Tanzania. METHODS: Convergent mixed method design was employed in this study. Questionnaires were used to examine the trajectory and trend of the selected MCH indicators using the baseline and end-line data for the implemented CBI interventions. Data was also collected through in-depth interviews and focus group discussions, mainly with implementers of the interventions from the community and the implementation research team. The collected quantitative data was analysed using IBM SPSS, while qualitative data was analysed thematically. RESULTS: Antenatal care visits increased by 24% in Kilolo and 18% in Mufindi districts, and postnatal care increased by 14% in Kilolo and 31% in Mufindi districts. Male involvement increased by 5% in Kilolo and 13% in Mufindi districts. The uptake of modern family planning methods increased by 31% and 24% in Kilolo and Mufindi districts, respectively. Furthermore, the study demonstrated improved awareness and knowledge on matters pertaining to MCH services, attitude change amongst healthcare providers, and increased empowerment of women group members. CONCLUSION: Community-Based Interventions through participatory women groups are vital for increasing the uptake of MCH services. However, the success of CBIs depends on the wide array of contextual settings, including the commitment of implementers of the interventions. Thus, CBIs should be strategically designed to enlist the support of the communities and implementers of the interventions.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".