Mutuelles de santé à Bukavu en République Démocratique du Congo: facteurs favorables à l’utilisation des services de santé par des adhérents
Bibliographic record
Abstract
INTRODUCTION: This study highlights the determinants of the use of health services by adherents to the three mutual health insurances in the town of Bukavu in the Democratic Republic of the Congo. METHODS: We conducted a descriptive cross-sectional study, based on a perception survey among users of healthcare services affiliated to the mutual health insurances in the Bukavu health zones. The encoding and statistical analysis were carried out using the Epi INFO version 2010 software. RESULTS: The main determinants of the use of healthcare services by adherents to the mutual health insurances are: the member's place of residence, the level of education of the head of household, the previous experience of care in the healthcare structure partner of the mutual health insurances, the reputation of the structure partner of the mutual health insurances and the ability of households to pay the user fee. CONCLUSION: This study highlights that, beyond the financial barrier, the implementation of a mutual health organisation should promote a better regulation of the user fee and a good quality of care to meet the care needs of members. The factors emerging from the study as a major determinant of the use of health services by adherents to a mutual health insurance are often not taken into account in the implementation of mutual health insurance in contexts similar to those of Bukavu.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".