The Hidden Costs of a Free Caesarean Section Policy in West Africa (Kayes Region, Mali)
Bibliographic record
Abstract
The fee exemption policy for EmONC in Mali aims to lower the financial barrier to care. The objective of the study was to evaluate the direct and indirect expenses associated with caesarean interventions performed in EmONC and the factors associated with these expenses. Data sampling followed the case control approach used in the large project (deceased and near-miss women). Our sample consisted of a total of 190 women who underwent caesarean interventions. Data were collected from the health workers and with a social approach by administering questionnaires to the persons who accompanied the woman. Household socioeconomic status was assessed using a wealth index constructed with a principal component analysis. The factors significantly associated with expenses were determined using multivariate linear regression analyses. Women in the Kayes region spent on average 77,017 FCFA (163 USD) for a caesarean episode in EmONC, of which 70 % was for treatment. Despite the caesarean fee exemption, 91 % of the women still paid for their treatment. The largest treatment-related direct expenses were for prescriptions, transfusion, antibiotics, and antihypertensive medication. Near-misses, women who presented a hemorrhage or an infection, and/or women living in rural areas spent significantly more than the others. Although abolishing fees of EmONC in Mali plays an important role in reducing maternal death by increasing access to caesarean sections, this paper shows that the fee policy did not benefit to all women. There are still barriers to EmONC access for women of the lowest socio-economic group. These included direct expenses for drugs prescription, treatment and indirect expenses for transport and food.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".