Performance of primary care in different healthcare facilities: a cross-sectional study of patients’ experiences in Southern Malawi
Bibliographic record
Abstract
OBJECTIVE: In most African countries, primary care is delivered through a district health system. Many factors, including staffing levels, staff experience, availability of equipment and facility management, affect the quality of primary care between and within countries. The purpose of this study was to assess the quality of primary care in different types of public health facilities in Southern Malawi. STUDY DESIGN: This was a cross-sectional quantitative study. SETTING: The study was conducted in 12 public primary care facilities in Neno, Blantyre and Thyolo districts in July 2018. PARTICIPANTS: Patients aged ≥18 years, excluding the severely ill, were selected to participate in the study. PRIMARY OUTCOMES: We used the Malawian primary care assessment tool to conduct face-to-face interviews. Analysis of variance at 0.05 significance level was performed to compare primary care dimension means and total primary care scores. Linear regression models at 95% CI were used to assess associations between primary care dimension scores, patients' characteristics and healthcare setting. RESULTS: The final number of respondents was 962 representing 96.1% response rate. Patients in Neno hospitals scored 3.77 points higher than those in Thyolo health centres, and 2.87 higher than those in Blantyre health centres in total primary care performance. Primary care performance in health centres and in hospital clinics was similar in Neno (20.9 vs 19.0, p=0.608) while in Thyolo, it was higher at the hospital than at the health centres (19.9 vs 15.2, p<0.001). Urban and rural facilities showed a similar pattern of performance. CONCLUSION: These results showed considerable variation in experiences among primary care users in the public health facilities in Malawi. Factors such as funding, policy and clinic-level interventions influence patients' reports of primary care performance. These factors should be further examined in longitudinal and experimental settings.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".