Do waiting list initiatives discriminate in favour of those in a higher socioeconomic group?
Bibliographic record
Abstract
The UK has a publicly funded health care system with open access to all. In the past, demand for services overwhelmed the resources available. Recent government initiatives have attempted to address this. To achieve shorter waiting times (and guaranteed waiting times), access to additional services has been purchased from the private sector under short-term initiatives, often at sites firth of the home health board. There has been a suspicion that patients from higher socioeconomic groups have benefited differentially from this by rapid access to private health care facilities, due to ease of transport. The aim of this study was to analyse whether a patient's socioeconomic group influenced their access to, and place of, surgery. Patients undergoing a primary total hip or knee arthroplasty in a single health region over a three-year period were identified and their social group was determined by postcode address. Analysis of 3888 patients operated on in four different treatment centres comparing the distribution of patients according to their social group, revealed no bias in the provision of treatment. The study group was comparable to the control population in that health region. In conclusion, the introduction of health policies to reduce time to orthopaedic treatment within one health board area has not resulted in patient bias.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".