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?.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".