The development and implementation of a community engagement strategy to improve maternal health in southern Mozambique
Bibliographic record
Abstract
Delays to seek medical help can contribute to maternal deaths particularly in community settings at home or on the road to a health facility. Community engagement (CE) can improve care-seeking behaviours and complements community-based interventions strengthening maternal health. The purpose of this paper is to describe the process undertaken to develop and implement a large-scale community engagement strategy in rural southern Mozambique. The CE strategy was developed within the context of the "Community-Level Interventions for Pre-eclampsia" (NCT01911494) conducted between 2015-2017 in southern Mozambique. Key CE messages included pregnancy complications and their warning signs, including pre-eclampsia and eclampsia, as well as emergency readiness, birth preparedness, decision-making mechanisms, transport options and information about the trial. CE meeting logs were used to record quantitative and qualitative information on demographic data and feedback. Quantitative data was analyzed using RStudio (RStudio Inc, Boston, United States) and community feedback was qualitatively analyzed on NVivo12 (QSR International, Melbourne, Australia). CE activities reached 19,169 participants during 4,239 meetings. CE activities were reported to be well received by community members though there was a relatively lower participation of men (3565 /18.6%). The use of recognized local leaders and personnel, such as community leaders, nurses and community health workers, allowed for greater acceptance of CE activities and maximized coverage of health messages in the community setting. Our CE strategy was effective in integrating maternal health promoting activities in routine care of community health workers and nurses in the area. Understanding district differences, engaging husbands, partners, mothers-in-law and community-level decision-makers to build local support for maternal health and flexibility to tailor messages to local needs were important in developing sustainable forms of CE. Better strategies are needed to effectively engage men in maternal health promotion who were less available due to working outside of the home or neighbourhoods.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".