Bibliographic record
Abstract
Separating issues of funding (i.e., who pays for healthcare) and delivery (i.e., who owns and administers theinstitutions providing care) helps to inform debates about health care systems. Funding for health care can come through private sources, primarily administered through insurance companies, or through public payment, by governments using tax dollars. Care can be delivered at private for-profit institutions that are owned by investors; private not-for-profit institutions that are owned by com-munities, religious organizations or philanthropic groups; or public health care institutions owned and administered by the government. Canadian hospitals are publicly funded. In terms of de-livery, although they are commonly referred to as public institutions, Canadian hospitals are almost all owned and operated by private not-for-profit organizations.1 Canadian policy-makers continue to consider an expansion of private for-profit health care delivery, including private for-profit hospitals.1 We have previously demonstrated higher risk-adjusted death rates among patients receiving care at private for-profit hospitals than among patients at private not-for-profit hospitals in a comprehensive systematic review.2 Uncertainty remains, however, about the economic im-plications of these forms of health care delivery. Studies evaluating the economics of health care delivery usually evaluate costs, charges or payments for care.3 From the perspective of a service provider, costs represent how much the provider paid to provide care, charges represent how much the provider billed the payer, and payments represent how much the provider received for the care re-ceived. In the context of publicly funded health care, the central policy question is how much government will pay for care delivered by private for-profit versus private not-for-profit providers. We therefore undertook a systematic Payments for care at private for-profit and private not-for-profit hospitals: a systematic review and meta-analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.032 |
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".