From Measuring User Satisfaction to Improving Quality of Care: Community Verifications and Performance-Based Financing in District Hospitals in Mali
Bibliographic record
Abstract
Abstract Context: User satisfaction in health facilities is increasingly taken into account to improve the quality of care. In Mali, performance-based financing (PBF) aims to improve the quality of care by increasing the motivation, responsibility, and accountability of health workers. Community verification (CV) is one of the main functions of the PBF. CV makes it possible to monitor the effectiveness of the services reported by the health facilities and to conduct satisfaction surveys of the users who have received care. This paper aims to analyze the CV process during the implementation of the PBF in the Koulikoro region of Mali.Methods: We adopted a qualitative approach based on a multiple case study design corresponding to the district hospitals (DH) and stakeholder groups. We conducted thirty-nine semi-structured interviews with investigators, patients, and health workers in three of the ten DHs in the Koulikoro region. We designed three interview guides and inductively analyzed our data.Results: The CV process was well perceived and welcomed. However, the vast majority of interviewers reported difficulties locating some patients in the community for several reasons: incorrect spelling of names, illegible cards, lack of telephone contact information and incomplete information on the cards, and cases of homonymity. All of this was due to poor record-keeping at the health facilities. Some investigators fraudulently filled out forms without investigating users in the communities. They justified this by the lack of time available to them to conduct the CV. No feedback on the results of the CV was possible from the health workers.Conclusion: The CV is an essential component of PBF implementation. The validity, reliability, and efficiency of the measurement instruments must be considered in user-satisfaction surveys. In addition, emphasis should be placed on data analysis and the use of CV results.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".