Public Hospital Facilities Development Using Build-Operate-Transfer Approach: Policy Consideration for Developing Countries
Bibliographic record
Abstract
Background: Advantages and limitations of build-operate-transfer (BOT) contracts in various forms of public-private partnership (PPP) arrangements have not been studied. Objectives: This study is the first of its kind to determine the framework, advantages, and limitations of BOT contracts for health care projects in selected countries. Methods: A comparative design was employed to identify factors affecting the development of medical facilities through the adoption of PPPs and the implementation of BOT contracts. England, Spain, Australia, Turkey, and Canada were selected, and data were gathered through well-known databases for the relevant studies. Electronic databases were searched using the keyword terms, “build-operate-transfer,” “public-private partnerships,” “health sector/health system,” “health care facilities,” “Spain,” “Canada,” “England or United Kingdom,” “Turkey,” and “Australia.” Results: The findings revealed that while there was insufficient information transparency for adoption of the BOT contract model in developing medical facilities and building new hospitals, some similarities were observed in its adoption in public fields. Adoption of the BOT contract model has been proven feasible in the selected countries for the health sector, in particular, for the development of new hospitals. These contracts are usually long-term in nature to provide the private sector with the chance to appropriately exploit the field. Different countries utilize this model to meet public regional and long-term health care needs, where the goal is not just a matter of seeking the private sector’s contribution. Conclusions: This study suggests that more information transparency is required for these types of contracts. Factors such as the term of the contract, the maintenance of the facilities built and their post-completion ownership status, facilities and credits offered to the private sector during the construction and operation phases, and the provision of financial and non-financial incentives to the private sector require deeper examination and should also be adjusted to the local contexts of the developing country.
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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 | 0.036 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".