Seeing double: expanding GP capacity through teamwork and redesign
Bibliographic record
Abstract
Like many practices, we’ve felt the pinch from a shrinking GP workforce. Over a period of some years we’ve suffered from GP turnover: younger GPs emigrating to Australia or Canada, with the more senior among us contemplating approaching retirements. Recruitment has faltered and failed. We responded with some radical ideas and embraced change. This year we merged with a ‘super practice’ to ensure our future viability and we also decided to recruit two advanced nurse practitioners to spread our workload. But we also came up with a novel idea to redesign the GP consultation involving our practice nurses, effectively expanding GP capacity. The idea was simple but challenging. For suitable patients, might it be possible to split the consultation such that history taking and basic observations or examination could be done by the nurse, with the GP then completing the consultation after a brief handover? The nurse then starts a new consultation in the next room — and so on. …
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".