Explaining Waiting Times Variations for Elective Surgery Across OECD Countries
Bibliographic record
Abstract
Waiting times for elective surgery are a significant health policy concern in approximately half of all OECD countries. The main objectives of the OECD Waiting Times project were to: i) review policy initiatives to reduce waiting times in 12 OECD countries; and ii) to investigate the causes of variations in waiting times for non-emergency surgery across countries. The first objective was addressed in an earlier report (Hurst and Siciliani, 2003; OECD Health Working paper, n.6). This report is devoted to the second objective. An interesting feature of OECD countries is that while some countries report significant waiting, others do not. Waiting times are a serious health policy issue in the 12 countries involved in this project (Australia, Canada, Denmark, Finland, Ireland, Italy, Netherlands, New Zealand, Norway, Spain, Sweden, and the United Kingdom). Waiting times are not recorded administratively in a second group of countries ... Dans pres de la moitie des pays de l’OCDE, les delais d’attente pour les interventions chirurgicales non urgentes constituent un important sujet de preoccupation pour les responsables de la politique de la sante. Le projet de l’OCDE sur ce sujet vise principalement les objectifs suivants : i) examiner les initiatives prises par les pouvoirs publics en vue de reduire ces delais d’attente dans douze pays Membres ; ii) rechercher les causes des differences observees d’un pays a l’autre quant a ces delais. Un precedent rapport a ete consacre au premier de ces objectifs (Hurst et Siciliani, 2003 ; document de travail de l’OCDE sur la sante, n°6). Le present document porte sur le second objectif. Il est interessant de noter que, si certains pays de l’OCDE font etat de delais d’attente non negligeables, ce n’est pas le cas pour d’autres. Ces delais posent un epineux probleme de fond en matiere de sante dans les douze pays qui participent au projet ...
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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".