Choosing between nurse-led and medical doctor-led from private for-profit versus non-for-profit health facilities: A household survey in urban Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Providers' qualification (Medical doctor [MD] or nurse); type of care facility ownership (for-profit [FP] or not-for-profit [NFP]) may all influence individuals' healthcare-seeking behavior and therefore merits empirical assessment to provide valuable evidence-informed policy orientation in the present context of private health system development. Previous studies have not examined these factors in combination, especially within the urban context of sub-Sahara Africa, where the private sector is rapidly growing. This study aims to explore factors associated with urban residents' preferences between private MD-led and private nurse-led outpatient care and how these factors vary by type of private health facility ownership (FP and NFP) and levels of disease severity (severe and non-severe cases). METHODS: A cross-sectional household survey was conducted in July-November 2011 on a random final sample of 2064 adults (646 households). We used a face-to-face interview to capture participants' choice of provider and their associated factors. A multivariable logistic regression was applied. RESULTS: For severe conditions, participants, almost equally sought FP and NFP facilities, only 36.4% preferred nurses compared to MDs, while for non-severe cases 53.2% preferred FP facilities and only 29.2% patronized nurses. For non-severe conditions, university educated were more likely to use MDs-led FP compared to nurse-led FP facilities (Odds Ratio [OR] = 4.66, 95% confidence interval [CI] = 2.62-8.30) and MD-led FP over MD-led NFP facilities (OR = 1.03, 95%CI = 1.01-1.04), for severe health conditions. Having insurance predicted MD-led FP preference over nurse-led FP. Furthermore, insurance predicted the preference for MD-led FP over MD-led NFP facilities. Employment did not distinguish participants' choice of provider. CONCLUSION: The findings suggest that, at different levels, MDs and nurses from FP and NFP facilities importantly contribute to health services delivery regardless of the severity of health conditions. The results offer some valuable evidence for policy orientation in the current rising tide of the private system, including workforce development, and practitioners' role definition. We suggested that health insurance mechanism would reinforce the private health services utilization and could enhance progress towards the attainment of Sustainable Development Goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".