How do community health actors explain their roles? Exploring the roles of community health actors in promoting maternal health services in rural Ethiopia
Bibliographic record
Abstract
BACKGROUND: Maternal and child morbidity and mortality remains one of the most important public health challenges in developing countries. In rural settings, the promotion of household and community health practices through health extension workers in collaboration with other community members is among the key strategies to improve maternal and child health. Little has been studied on the actual roles and contributions of various individuals and groups to date, especially in the rural areas of Ethiopia. In this study, we explored the role played by different actors in promoting ANC, childbirth and early PNC services, and mainly designed to inform a community based Information, Education & Communication intervention in rural Ethiopia. METHODS: An exploratory qualitative study was conducted on 24 in-depth interviews with health extension workers, religious leaders, women developmental army leaders, and selected community members; and 12 focus group discussions, six with female and six with male community members. Data was captured using voice recorders and field notes and transcribed verbatim in English, and analyzed using Atlas.ti software. Ethical approval for the fieldwork was obtained from Jimma University and the University of Ottawa. RESULTS: Participants described different roles and responsibilities that individuals and groups have in promoting maternal/child health, as well as the perceived roles of family members/husband. Commonly identified roles included promotion of health care services; provision of continuous support during pregnancy, labour and postnatal care; and serving as a link between the community and the health system. Participants also felt unable to fully engage in their identified roles, describing several challenges existing within both the health system and the community. CONCLUSIONS: Involvement of different actors based on their areas of focus could contribute to community members receiving health information from people they trust more, which in turn is likely to increase use of services. Therefore, if our IEC interventions focus on overcoming challenges that limit actors' abilities to engage effectively in promoting use of MCH services, it will be feasible and effective in rural settings, and these actors can become an epicenter in providing community based intervention in using ANC, childbirth and early PNC services.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".