Experiences of selected countries in the use of public-private partnership in hospital services provision.
Bibliographic record
Abstract
OBJECTIVE: To review the experiences of selected countries in the use of public-private partnership in the provision of hospital services. METHODS: This comparative study was conducted in 2015 in Iran. To collect data, valid databases as well as articles, theses, reports and related books in the field of private-sector partnership in hospital services were employed. Using purposive sampling, countries such as the United Kingdom, Spain, Canada, Turkey, Australia and Lesotho, which had successful experiences in the field of application of the public-private partnership in hospital services, were included. Likewise, the only experience in Iran in this field was also reviewed. Studies done between 1980 and 2015 were examined. The results obtained from each country were compared. RESULTS: Implementing public-private partnership had great and valuable outcomes and achievements for governmental hospitals. Moreover, clinical and nonclinical service delivery, hospital utilisation and management along with building, repairing and supportive operations through public-private partnership contracts can be differently divided among the partners. Furthermore, duration of the projects ranged from 12 to 40 years in different countries, depending on the type of the model used. CONCLUSIONS: A successful experience in the use of the public-private partnership in the provision of hospital services was observed.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".