Midwives providing maternal health services to poor women in the private sector: is it a financially feasible model?
Bibliographic record
Abstract
Governments in many low- and middle-income countries have increasingly turned to the private sector to address the gap in skilled birth attendance in rural areas. They draw on limited, but emerging evidence that the poor also seek private healthcare services. A question not addressed in this policy and strategy is: Can poor women pay the fees required for private-sector maternity care providers to financially sustain their practices? This article examined the financial viability of private-sector midwifery practices established to provide skilled birth services to Afghan refugee women in Baluchistan, Pakistan. An international non-governmental organization established 45 midwifery practices as part of a poverty alleviation project aimed at providing market-based solutions for female poverty. A retrospective micro-cost analysis was conducted on a sample of 11 practices. In-depth interviews were conducted with 33 stakeholders to explore the midwives' experiences of operating private practices, and the facilitators and barriers they experienced. The single midwife-practices saw a mean of 8.7 ANC patients (range 1-19), attended 2.9 births (range 0-10) and provided care to 1.6 postnatal patients (range 0-7). The average net income of the 11 practices in May 2014 was US$81, but the median was just US$12. To contextualize these incomes, the midwives earned, on average, 25% of Pakistan's minimum monthly living wage. The financial analysis showed only 3 out of 11 sampled practices could be considered financially viable. The qualitative data revealed that even in practices with reasonable client volumes, the patients' inability to pay was the critical factor in the midwife practices' low net incomes. The research provides empirical evidence of a potential pitfall of private funding models in resource-poor settings where providers rely on impoverished clients to pay user-fees. Such financial models essentially shift the government's responsibility to provide safe childbirth services onto providers who can least afford to offer such care.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".