Toward communities as systems: a sequential mixed methods study to understand factors enabling implementation of a skilled birth attendance intervention in Nampula Province, Mozambique
Bibliographic record
Abstract
BACKGROUND: Skilled birth attendance, institutional deliveries, and provision of quality, respectful care are key practices to improve maternal and neonatal health outcomes. In Mozambique, the government has prioritized improved service delivery and demand for these practices, alongside "humanization of the birth process." An intervention implemented in Nampula province beginning in 2009 saw marked improvement in institutional delivery rates. This study uses a sequential explanatory mixed methods case study design to explore the contextual factors that may have contributed to the observed increase in institutional deliveries. METHODS: A descriptive time series analysis was conducted using clinic register data from 2009 to 2014 to assess institutional delivery coverage rates in two primary health care facilities, in two districts of Nampula province. Site selection was based on facilities exhibiting an initial increase in institutional deliveries from 2009 to 2011, similarity of health system attributes, and accessibility for study participation. Using a modified Delphi technique, two expert panels-each composed of ten stakeholders familiar with maternal health implementation at facility, district, provincial, and national levels-were convened to formulate the "story" of the implementation and to identify contextual factors to use in developing semi-structured interview guides. Thirty-four key informant interviews with facility MCH nurses, facility managers, traditional birth attendants, community leaders, and beneficiaries were then conducted and analyzed using the Consolidated Framework for Implementation Research through inductive and deductive coding. RESULTS: The two sites' skilled birth attendance coverage of estimated live births reached 80 and 100%, respectively. Eight contextual and human factors were found as dominant themes. Though both sites achieved increases, implementation context differed significantly with compelling examples of both respectful and disrespectful care. In one site, facility and community actors worked together as complementary systems to sustain improved care and institutional deliveries. In the other, community actors sustained implementation and institutional deliveries largely in absence of health system counterparts. CONCLUSION: Findings support global health recommendations for combined health system and community interventions for improved MNH outcomes including delivery of respectful care, and further suggest the capacity of communities to act as systems both in partnership to and independent of the formal health system.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".