Reducing waiting times associated with an integrated child health service
Bibliographic record
Abstract
Following an increase in average waiting times associated with a child health service in East London, an initiative to rapidly reduce the numbers of children waiting long periods following a referral was undertaken over the period May to June 1999. A multidisciplinary cooperative approach was adopted operating within the existing available resources and involved medical, nursing, managerial and administrative staff. The initiative involved a review of the accuracy of the waiting list, followed by an invitation to remaining patients to provide an option of continuing to wait to be seen or offering attendance at a rapid response clinic associated with reduced waiting and consultation times. Half-hourly appointments were routinely offered instead of hourly appointments and proformas were adopted for history taking and onward referrals to save time spent on administration. A total of 162 patients were seen over the course of a month and a satisfaction questionnaire completed by relatives indicated a preference for the new service. The mean waiting time was reduced to under a quarter of the time at the start of the initiative to a mean of less than two months. The purpose of the study was to see if the waiting list could be reduced by using existing staff. We wanted to ascertain the parents' views whether shorter waits and shorter consultation periods were acceptable, and to ascertain if the waiting list could be kept down or whether the waiting list would rapidly recur after the rapid response clinics stopped. The findings are discussed in relation to initiatives elsewhere and the need to maintain a high quality service.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".