The influence of community factors in the implementation of community-based interventions to improve antenatal care: a qualitative study based on the IMCHA programme in Tanzania
Bibliographic record
Abstract
BACKGROUND: Efforts to improve antenatal care have been heightened to reduce global maternal deaths. In resource-limited settings, community-based interventions play a pivotal role in improving antenatal care services. However, effective implementation of community-based interventions is influenced by prevailing community-related factors. Drawing from the community-based interventions implemented in Iringa Region in Tanzania, this paper underscores how community factors influence implementation and ultimate improvement of antenatal care services. METHODS: A qualitative case study design was employed using in-depth interviews, focus group discussions and document reviews. Data was collected in Kilolo and Mufindi districts in Iringa Region where community-based interventions were implemented. A total of one hundred and forty-six (146) participants were involved in the study. Eighty-six (86) participants were interviewed and sixty (n = 60) participated in focus group discussions. Data were analysed thematically and manually by categorizing and coding emerging issues to facilitate analysis and interpretation. RESULTS: Key factors that influenced the implementation of the community-based interventions were the community readiness to adopt the interventions and effective local administrative systems. Stakeholders' engagement and local health system support were also pivotal for improving antenatal care services. However, the physical environment, bullying of implementers of interventions and family-related challenges constrained the implementation of the interventions. CONCLUSION: This study has shown that the performance of community-based interventions is highly influenced by community-related factors. More specifically, inadequate community engagement may lead to community members' reluctance to adopt implemented interventions. Therefore, in-depth understanding and adequate management of community engagement are important during the planning, development and implementation of community-based 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".