Organizational analysis of maternal mortality reduction programs in Madagascar
Bibliographic record
Abstract
Little is known about the organizational factors involved in policy creation and programs implementation aimed at reducing maternal mortality in Madagascar. A qualitative case study was performed to investigate organizational factors influencing the health system's capacity to elaborate and implement maternal mortality reduction programs. Semi-structured interviews were conducted with 53 participants. A conceptual framework based on Gamson's coalition theory and Hinings and Greenwood's archetypes concept was used. Three major conclusions emerge: the Ministry of Health is a poor leader in the development of national strategies, due to its dependency on external financial resources and expertise, and because of poor transmission of key information from the field; at a meso level (regions and districts), the capacity to adapt programs is highly dependent on the collaboration with NGOs; at the micro level, there are few incentives provided to field workers to participate in a collective effort and little attempt to exploit complementarities between scare resources. The Madagascar health system should consider the need for improvement in data analysis capacity, and implementing behavior-changing tools suitable for stimulating providers who work inside and outside the health care system, to participate to a coordinated collective effort.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".