Quality of inpatient care in public and private hospitals in Sri Lanka
Bibliographic record
Abstract
OBJECTIVE: To compare the quality of inpatient clinical care in public and private hospitals in Sri Lanka. METHODS: A retrospective, cross-sectional comparison was done of inpatient quality, in a sample of 11 public and 10 private hospitals in three of 25 districts. Data were collected for 55 quality indicators from medical records of 2523 public and 1815 private inpatient admissions. These covered treatment of asthma, acute myocardial infarction (AMI), childbirth and five other conditions, along with outcome indicators, and medicine prescribing indicators. RESULTS: Overall quality scores were better in the public sector than the private sector (77 vs 69%). Performance was similar for management of AMI and childbirth and somewhat better in the private sector for management of asthma. The public sector performed better in those indicators that are not constrained by resources (94 vs 81%), but worse in indicators that are highly resource intensive (10 vs 31%). Quality was comparable in assessment and investigation, but the public sector performed better in treatment and management (70 vs 62%) and drug prescribing (68 vs 60%), and modestly worse in terms of outcomes (92 vs 97%). CONCLUSIONS: For a range of indicators where comparisons were possible, quality of inpatient clinical care in Sri Lanka was comparable to levels reported from upper-middle income Asian countries, and often approaches that in developed countries, although the findings cannot be generalized. Quality in the public sector is better than in the private sector in many areas, despite spending being substantially less. Quality in public hospitals is resource constrained, and needs greater government investment for improvement, but when resource limitations are not critical, the public sector appears able to deliver equal or better quality than the private sector. Overall similarities in quality between the two sectors suggest the importance of physician training and other factors.
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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 | 0.002 | 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 | 0.001 |
| 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".