Leveraging health financing, digital health and self-care approaches to strengthen maternal health journeys in India: perspectives from Assam
Bibliographic record
Abstract
Maternal morbidity and mortality in India continue to be high in populations and places with limited access to quality health services. Major barriers include out of pocket expenditure, lack of autonomy and information around maternal health services and weak implementation of pro-poor policies. Addressing demand-side barriers and enablers is critical to improving healthcare uptake and healthcare adherence along the pregnancy-postnatal continuum. This paper describes three well known operational spaces, maternal health financing, digital health, and self-care interventions within the Indian context including pro-poor maternal health policies, mobile health ecosystems and networks, and self-care opportunities that promote women's knowledge, choice, self-efficacy, and autonomy. These are expanded on to identify additional opportunities to improve access to MH services. Finally, the authors describe a new digital health intervention using a chat-based digital support system that has the potential to reduce barriers that women face in seeking and receiving quality MH services in Assam and elsewhere. Future work on how to implement such a combined approach need to account for multiple contextual factors, including understanding the nature and success of national pro-poor MH policies in each state, how the public and private health systems function and interact, social determinants of health as well as engaging women in the process to improve maternal and newborn health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".