Comprehensive review of the evidence regarding the effectiveness of community–based primary health care in improving maternal, neonatal and child health: 8. summary and recommendations of the Expert Panel
Bibliographic record
Abstract
BACKGROUND: The contributions that community-based primary health care (CBPHC) and engaging with communities as valued partners can make to the improvement of maternal, neonatal and child health (MNCH) is not widely appreciated. This unfortunate reality is one of the reasons why so few priority countries failed to achieve the health-related Millennium Development Goals by 2015. This article provides a summary of a series of articles about the effectiveness of CBPHC in improving MNCH and offers recommendations from an Expert Panel for strengthening CBPHC that were formulated in 2008 and have been updated on the basis of more recent evidence. METHODS: An Expert Panel convened to guide the review of the effectiveness of community-based primary health care (CBPHC). The Expert Panel met in 2008 in New York City with senior UNICEF staff. In 2016, following the completion of the review, the Panel considered the review's findings and made recommendations. The review consisted of an analysis of 661 unique reports, including 583 peer-reviewed journal articles, 12 books/monographs, 4 book chapters, and 72 reports from the gray literature. The analysis consisted of 700 assessments since 39 were analyzed twice (once for an assessment of improvements in neonatal and/or child health and once for an assessment in maternal health). RESULTS: The Expert Panel recommends that CBPHC should be a priority for strengthening health systems, accelerating progress in achieving universal health coverage, and ending preventable child and maternal deaths. The Panel also recommends that expenditures for CBPHC be monitored against expenditures for primary health care facilities and hospitals and reflect the importance of CBPHC for averting mortality. Governments, government health programs, and NGOs should develop health systems that respect and value communities as full partners and work collaboratively with them in building and strengthening CBPHC programs - through engagement with planning, implementation (including the full use of community-level workers), and evaluation. CBPHC programs need to reach every community and household in order to achieve universal coverage of key evidence-based interventions that can be implemented in the community outside of health facilities and assure that those most in need are reached. CONCLUSIONS: Stronger CBPHC programs that foster community engagement/empowerment with the implementation of evidence-based interventions will be essential for achieving universal coverage of health services by 2030 (as called for by the Sustainable Development Goals recently adopted by the United Nations), ending preventable child and maternal deaths by 2030 (as called for by the World Health Organization, UNICEF, and many countries around the world), and eventually achieving Health for All as envisioned at the International Conference on Primary Health Care in 1978. Stronger CBPHC programs can also create entry points and synergies for expanding the coverage of family planning services as well as for accelerating progress in the detection and treatment of HIV/AIDS, tuberculosis, malaria, hypertension, and other chronic diseases. Continued strengthening of CBPHC programs based on rigorous ongoing operations research and evaluation will be required, and this evidence will be needed to guide national and international policies and programs.
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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 | 0.068 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".