Engagement of private healthcare sector in reproductive, maternal, newborn, child and adolescent health in selected Eastern Mediterranean countries
Bibliographic record
Abstract
Background: The private healthcare sector in the Eastern Mediterranean Region (EMR) is active and growing, providing curative, preventive, and promotive services related to reproductive, maternal, newborn, child, and adolescent health (RMNCAH). Aims: To understand the contribution of formal for-profit private health-care sector in delivering RMNCAH services and explore best practices for improvement. Methods: Desk review of available literature from Saudi Arabia, Oman, Iraq, Egypt, Sudan, Yemen, Pakistan, and Islamic Republic of Iran, followed by stakeholder interviews in Iraq, Pakistan, and Oman were carried out. Directed content analysis using Maxqda 2020 was performed, and information was triangulated according to a priori themes: governance, health information systems, financing, and service delivery related to RMNCAH. Results: Formal and informal public-private partnerships exist in RMNCAH but lack a strategic roadmap to guide collaboration. The private healthcare sector is minimally represented in the main policy stream at national and subnational levels due to resistance from the private and public sectors. They are weak in collecting, maintaining, and sharing health information. Data on abortion and postabortion complications are scarce. Various models of supply and demand financing (voucher schemes, private and social health insurance) related to antenatal care and contraception have been implemented in the EMR. Despite the higher cost of care in the private sector, limited training of providers, ill-defined service delivery packages, and lack of continuity-of-care and team-based approaches, the private sector remains the predominant sector providing RMNCAH services in the EMR. Conclusion: Partnering with the private sector has huge untapped potential that should be harnessed by national governments for expanding RMNCAH services and progressing towards Universal Health Coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".