Space, place and (waiting) time: reflections on health policy and politics
Bibliographic record
Abstract
Health systems have repeatedly addressed concerns about efficiency and equity by employing trans-national comparisons to draw out the strengths and weaknesses of specific policy initiatives. This paper demonstrates the potential for explicit historical analysis of waiting times for hospital treatment to add value to spatial comparative methodologies. Waiting times and the size of the lists of waiting patients have become key operational indicators. In the United Kingdom, as National Health Service (NHS) financial pressures intensified from the 1970s, waiting times have become a topic for regular public and political debate. Various explanations for waiting times include the following: hospital consultants manipulate NHS waiting lists to maintain their private practice; there is under-investment in the NHS; and available (and adequate) resources are being used inefficiently. Other countries have also experienced ongoing tensions between the public and private delivery of universal health care in which national and trans-national comparisons of waiting times have been regularly used. The paper discusses the development of key UK policies, and provides a limited Canadian comparative perspective, to explore wider issues, including whether 'waiting crises' were consciously used by policymakers, especially those brought into government to implement new economic and managerial strategies, to diminish the autonomy and authority of the medical professional in the hospital environment.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".