Rationalising Waiting Lists in Health Care Delivery: an international comparison
Bibliographic record
Abstract
Rationalizing waiting times in health caRe deliveRy: an inteRnational compaRison 1. introduction waiting lists are one of the major problems in publicly funded system, engendering discomfort and dissatisfaction.in absence of price, however, waiting lists are 'necessary' because they act as a rationing tool to balance supply-side and demand-side, thus achieving allocative efficiency.public funding, however, aims at ensuring equity also, that is at guaranteeing access based on need and not on purchasing power.if waits are too long, people who can pay will address private healthcare, while this is not possible for the poorest ones, even if their need is equally urgent. in this case, the rationing tool would be again the price, exactly what public intervention intended to avoid.the paper tries to outline how efficiency can be achieved together with equity also in current practice, with reference to a particular high demand health care service, elective (non-urgent) surgery.waiting times for elective surgery are a main health concern in approximately half of the oecd countries (siciliani and hurst, 2005).at first glance, it seems that facing waiting list problems is not too difficult as it would be sufficient only to increase the supply for health care delivery.as this may be difficult due to budget restriction, waiting lists are seen mainly as a symptom of insufficient capacity. the model presented demonstrates that paradoxically increasing capacity would not be the solution, because in brief the demand increases again more than supply does and waiting times attain levels similar to those before the capacity increased.Following the experience put forward in some countries, in particular canada, australia, new zealand and recently also italy, the most appropriate policy seems, instead, to be on the demand-side, through prioritization of needs.priority settings, however, is not easy to implement in the practice: in section 3 two different approaches are compared (tanfani and testi, 2004), whereas in section 4 a successful experience in
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".