Bibliographic record
Abstract
INTRODUCTION: Many family doctors in Prince Edward Island, Canada, use two or more consulting rooms, with patients initially assessed by office nurses. They are typically Licenced Practical Nurses (LPNs) with 2 years of non-university diploma-level training. Standards of assessment are highly variable, ranging from a brief chat/presenting symptoms/vital signs right through to excellent histories and physical exams. There has been little or no critical evaluation of this way of working - surprisingly so, given public concern about healthcare costs. As a first step, we decided to audit the effectiveness of skilled nurse assessment by looking at diagnostic accuracy and 'value added'. METHODS: We examined 100 consecutive assessments per nurse and recorded if diagnosis/diagnoses accorded with doctor findings. As a secondary check, we reviewed each file after 6 months to see if the doctor had missed anything. We also looked at other items that the doctor would probably have missed if seeing the patient without nurse assessment, eg screening advice, counselling, social welfare advice, and education on self-management of minor illness. RESULTS: As yet incomplete but look interesting - will be available in next few weeks. DISCUSSION: We initially did a 1-day pilot study in another location with a one doctor/two nurse collaborative team. We easily saw 50% more patients and we improved quality of care compared with the usual routine. We then moved to a new practice to road-test this approach. Results are presented.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".