Bibliographic record
Abstract
Introduction - international experience of rationing (or priority setting). Part 1 How to set priorities: setting priorities - what is holding us back inadequate information or inadequate institutions?. Part 2 Governments and rationing: developments in the Nordic countries goodbye to the simple solutions reactivation of the prioritization process in Finnish health care Israel's basic basket of health services - the importance of being explicitly implicit setting priorities American style. Part 3 Priorities in developing countries: health priority dilemmas in developing countries public health priorities and the social determinants of ill health. Part 4 Ethical dilemmas: accountability for reasonableness in private and public health insurance tragic choices in health care - lessons from the Child B case fairness as a problem of love and the heart - a clinician's perspective on priority setting the ethics of decentralizing health care priority setting in Canada. Part 5 Techniques for determining priorities: priority setting and health technology assessment - beyond evidence based medicine and cost effectiveness analysis the rationing of surgery - clinical judgement versus priority access scoring. Part 6 Involving the public: public involvement in health care priority setting - are the methods appropriate and valid? rationing health care in New Zealand how the public has a say explicit rationing, deprivation disutility and denial disutility - evidence from a qualitative study. Part 7 Rationing specific treatments: priority setting in practice when sentiments run high - the Di Bella case and others increasing demand for accountability - is there a professional response? conclusion - where are we now?.
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 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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".