Learning from Excellence: the ‘Yaytix’ programme
Bibliographic record
Abstract
BACKGROUND AND AIMS: to learn from excellence and correct the imbalance of negative to positive feedback in the context of hospital practice. METHODS AND RESULTS: Using a questionnaire, we surveyed staff on existing feedback mechanisms and morale. We then introduced a system where staff recorded and commented on examples of excellence in practice. Recipients and their supervisors received copies of these reports and the feedback was analysed and discussed with senior staff (consultant, senior charge nurse, managers). We re-audited the staff two months after starting this project and noted improvements in staff morale and in positive reporting. CONCLUSIONS: This project has improved the process of giving and learning from positive feedback and had a significant impact on staff morale. We can also demonstrate an example of improved clinical practice (from feedback received) and will now attempt to measure clinical outcomes with a new prospective study. Finally, we hope to set up a regional programme of reporting excellence in South-East Scotland.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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; both teacher heads agree on what is shown here.
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".