Talking health: trusted health messengers and effective ways of delivering health messages for rural mothers in Southwest Ethiopia
Bibliographic record
Abstract
BACKGROUND: Access to trusted health information has contribution to improve maternal and child health outcomes. However, limited research to date has explored the perceptions of communities regarding credible messenger and messaging in rural Ethiopia. Therefore, this study aimed to explore sources of trusted maternal health information and preferences for the mode of delivery of health information in Jimma Zone, Ethiopia; to inform safe motherhood implementation research project interventions. METHOD: An exploratory qualitative study was conducted in three districts of Jimma Zone, southwest of Ethiopia, in 2016. Twelve focus group discussions (FGDs) and twenty-four in-depth interviews (IDIs) were conducted among purposively selected study participants. FGDs and IDIs were conducted in the local language, and digital voice recordings were transcribed into English. All transcripts were read comprehensively, and a code book was developed to guide thematic analysis. Data were analyzed using Atlas.7.0.71 software. RESULT: Study Participants identified as Health Extension Workers (HEWs) and Health Development Army (HDA) as trusted health messengers. Regarding communication channels, participants primarily favored face-to-face/interpersonal communication channels, followed by mass media and traditional approaches like community conversation, traditional songs and role play.In particular, the HEW home-to-home outreach program for health communication helped them to build trusting relationships with community members; However, HEWs felt the program was not adequately supported by the government. CONCLUSION: Health knowledge transfer success depends on trusted messengers and adaptable modes. The findings of this study suggest that HEWs are a credible messenger for health messaging in rural Ethiopia, especially when using an interpersonal message delivery approach. Therefore, government initiatives should strengthen the existing health extension packages by providing in-service and refresher training to health extension workers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".