Performance-based financing, health sectors, and health seeking behavior of women attending antenatal care and skilled birth delivery: evidence from Cameroon
Bibliographic record
Abstract
Introduction: High maternal mortality rates (up to 536/100,000 live births) remain a concern in Came roon, despite multiple intervention strategies targeting antenatal care (ANC) and delivery.Up to 90% of women attend at least one ANC appointment, but only about 40% have 4 or more ANC visits, and some women who receive ANC prefer to deliver using a traditional birth attendant.Improvement in the quality of care by providers, incentivised by performancebased financing (PBF), could affect women's healthseeking behaviour over time and have an impact on the utilisation of services. Material and methods:We conducted a crosssectional survey of 848 pregnant women attending ante natal clinics in 3 districts in the southwest region of Cameroon in AprilAugust 2021.The survey and the qualitative questions were informed by the Andersen behavioural model.Descriptive analyses were conducted, together with logistic regression analysis.Followup focusgroup discussions were conduct ed with a subset of the women from the survey responses who had used ANC and delivery services before and after the implementation of PBF, and there was an exploration of providers' experiences of healthseeking behaviour of women attending ANC in the time of COVID19.Results: Responses from 735 women were included in the quantitative analysis.Cost and quality of care are important determining factors in the choice of seeking care for antenatal care and skilled birth deli very; however, quality of care (with a focus on patientprovider communication) is important in women's decision to use the same health facility for subsequent skilled birth delivery.Conclusions: The heterogenous nature of healthseeking behaviour calls for specific intervention strate gies tailored to specific health facilities and districts.PBF has the potential to improve quality of care to stimulate behaviour change in seeking care amongst poor and vulnerable women, but there is a need for proper definition and identification of the poor and vulnerable.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".