Policy strategies to reduce waits for elective care: a synthesis of international evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This synthesis seeks to assess and explain the effectiveness of policy interventions to reduce elective wait times or lists. PubMed, EMBASE, EconLit, and grey literature were systematically searched for relevant studies and reviews. Strategies with the strongest evidence base include paying for activity, buying capacity locally and setting targets with strong incentives. There is also evidence for improving the use of existing capacity. Limiting demand through rationing can reduce waits, but is ethically problematic. Short-term injections of funding, cross-border treatment schemes, unenforced targets and promotion of private health insurance had the weakest evidence. Available evidence favours options that act fairly directly on supply, demand or local organizations' behaviour, over indirect strategies that depend on a 'domino effect'. Further research is needed to determine how to achieve major, system-wide improvements in the use of capacity.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it