A systematic review of the accessibility, acceptability, safety, efficiency, clinical effectiveness and cost-effectiveness of private providers of elective surgical services compared with public providers
Bibliographic record
Abstract
Abstract Many publicly funded health systems use a mix of privately and publicly operated providers of care to deliver elective surgical services. We review the role of private elective surgical provision within publicly funded health systems in high-income countries. The outcomes evaluated include accessibility, acceptability, safety, clinical effectiveness, efficiency, and cost/cost-effectiveness. Twenty-seven articles met the review inclusion criteria. We found mixed results across each of our reported outcomes. Wait times were shorter for patients treated in private facilities in most studies, and inequalities by age and socioeconomic deprivation were found to increase with private provision in some studies. Acceptability results were mixed, with most studies finding no differences between public and private provision and others finding higher satisfaction at public facilities. The results for safety outcomes were divided, but most studies that found improved safety outcomes in private facilities, noting that private patients had a lower preoperative risk of complications. Clinical effectiveness was similar in most studies, with differences in outcomes mainly attributed to patient selection or prosthesis choice. Very few studies reported cost and cost-effectiveness outcomes, and just two included studies concluded that private facilities are economically viable.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".