Impact of NHS walk-in centres on primary care access times: ecological \nstudy
Bibliographic record
Abstract
Objective: To examine whether walk-in centres contribute to shorter \nwaiting times for a general practice appointment. \n \nDesign: Ecological study. \n \nSetting: 2 509 general practices in 56 primary care trusts in England; \n32 walk-in centres within 3 km of one of these practices. \n \nMain outcome measure: Waiting time to next available general \npractitioner appointment (April 2003 to December 2004), from national \nmonthly primary care access survey. \n \nResults: The percentage of practices achieving the target waiting time \nof less than 48 hours to see a general practitioner increased from 67% \nto 87% over the 21 month study period (adjusted odds ratio 1.07 (95% \nconfidence interval 1.06 to 1.08) per increase in month). \nAchievement \nof the waiting time target decreased with increasing multiple \ndeprivation (0.57 (0.49 to 0.67) for most versus least deprived third) \nand increased with increasing practice population size (1.02 (1.00 to \n1.04) per 1000 increase). No evidence was found that increasing \ndistance from a walk-in centre was associated with decreasing odds of \nachieving the waiting time target (1.00 (0.99 to 1.01) per km \nincrease). Increasing "exposure" to a walk-in centre, modelled with a \ndistance decay function based on attendance rates, also showed little \nevidence of association with achievement of the waiting time target \n(1.02 (0.97 to 1.08) for interquartile range increase). No evidence \nexisted that the rate of increase in achieving the 48 hour target over \ntime was enhanced by proximity or "exposure" to a walk-in centre. \n \nResults: were similar when the analysis was rerun with data for 2003 \nonly (done because pressure in 2004 to meet the government's deadline \nmight have led to other changes that could have masked any walk-in \ncentre effect). \n \nConclusions: No evidence existed that walk-in centres shortened waiting \ntimes for access to primary care, and the results do not support the \nuse of walk-in centres for this purpose.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".