Aligning public financial management system and free healthcare policies: lessons from a free maternal and child healthcare programme in Nigeria
Bibliographic record
Abstract
BACKGROUND: Relatively little is known about how public financial management (PFM) systems and health financing policies align in low- and middle-income countries. This study assessed the alignment of PFM systems with health financing functions in the free maternal and child healthcare programme (FMCHP) of Enugu State, Nigeria. METHODS: Data were collected through quantitative and qualitative document review, and semi-structured, in-depth interview with 16 purposively selected policymakers involved in FMCHP. Data collection and analysis were by guided a framework for assessing alignment of PFM systems and health financing policies. Revenue and expenditure trend analyses were done using descriptive statistics and analysis of variance (ANOVA). Level of significance was set at ρ < 0.05. Qualitative data were analysed using a framework approach. RESULTS: The results showed that no more than 50% of FMCHP fund were collected despite that the promised fund remained unchanged since inception. Revenue generation significantly varied between 2010 and 2016 (ρ < 0.05). Level of pooling was limited by non-compliance with contribution rules, recurrent unauthorised expenditure and absence of expenditure caps. The unauthorised expenditure significantly varied between 2010 and 2016 (ρ < 0.05). Misalignment of budget monitoring and purchasing revealed absence of auditing and delays in provider payment. Refunds to providers significantly varied between 2010 and 2016 (ρ < 0.05) due to weak Steering Committee, weak vetting team, paper-based claims management and institutional conflicts between Ministry of Health and district-level officials. CONCLUSIONS: This study identified important lessons to align PFM systems and FMCHP. A realistic and evidence-informed budget and enforcement of contribution rules are critical to adequate and sustainable revenue generation. Clarity of roles for various FMCHP committees and use of clear resource allocation strategy would strengthen pooling and fund management. Enforcement of provider payment standards, regular auditing, and a stronger role for the parliament in budgetary processes are warranted.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".