What do we know about patient-provider interactions in Sub-Saharan Africa? a scoping review
Bibliographic record
Abstract
INTRODUCTION: patient-centred care has become a rallying call for improving quality and access to care in countries where health system responsiveness and satisfaction with health services remain low. Understanding patient-provider interactions is important to guide implementation of an effective patient-centred care approach in sub-Saharan Africa. This review aims to overcome this knowledge gap by synthesizing the evidence on patient-provider interactions in sub-Saharan Africa. METHODS: we conducted a scoping review using Arksey and O´Malley´s framework. We searched in eight databases and the grey literature. We conducted a thematic analysis using an inductive approach to assess the studies. RESULTS: of the 80 references identified through database searching, nine met the inclusion criteria. Poor communication and several types of mistreatment (service denial, oppressive language, harsh words and rough examination) characterize patient-provider interactions in sub-Saharan Africa. Nevertheless, some health providers offer support to patients who cannot afford their medical expenses, cost of transportation, food or other necessities. Maintaining confidentiality depends on the context of care. Some patients blamed health providers for consulting with the door open or carrying out concomitant activities in the consultation room. However, in the context of HIV care provision, nurses emphasized the importance of keeping their patients´ HIV status confidential. CONCLUSION: this review advocates for more implementation studies on patient-provider interactions in sub-Saharan Africa so as to inform policies and practices for patient-centred health systems. Decision-makers should prioritize training, mentorship and regular supportive supervision of health providers to provide patient-centred care. Patients should be empowered in care processes.
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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 | 0.016 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".