Community-Oriented Primary Health Care for Improving Maternal, Newborn, and Child Health
Bibliographic record
Abstract
Abstract Nearly 80% of the world’s population lives in low- and middle-income countries (LMICs) and these regions bear the greatest burden of maternal, neonatal, and child mortality, with most of the deaths occurring at home. Much of global maternal and child mortality is attributable to easily preventable and treatable conditions. However, the challenge lies in reaching the most vulnerable communities, especially the rural populations, making it imperative that maternal, newborn, and child health (MNCH) interventions focus on communities in tandem with facility-based strategies. There is widespread consensus that delivering effective primary health care (PHC) interventions through the continuum of care, starting from pregnancy to delivery and then to the newborn, infant, and the young child, is an integral component of health strategies in high-, middle- and low-income settings. Despite gaps in research, several effective community-based PHC approaches have been proven to impact MNCH positively. Implementation of these strategies is needed at scale in LMICs and in partnership with all stakeholders including the public and private sector. Community-based PHC, operating on the principles of community engagement and community mobilization, is now more critical than ever. Further robust studies are needed to evaluate certain strategies of community-based PHC and their impact on maternal and child health outcomes, such as the use of mobile technology and social franchises. Recognition of community health workers (CHWs) as a formal cadre and the integration of community-based health services within PHC are vital in strengthening efforts to impact maternal, neonatal, and child health outcomes positively. However, despite the importance of community-based PHC for MNCH in LMICs, the existence of a strong health system and skilled workforce is central to achieving positive health outcomes in these regions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".