First do no harm: Teaching and assessing the recognition and rescue of deteriorating patients to nursing students
Bibliographic record
Abstract
Failure to recognise and appropriately rescue the deteriorating patient is a global issue which has the potential to cause serious harm to patients. Such recognition and rescue of a deteriorating patient requires both technical and non-technical skills and there are multiple points for potential failure. The taking and recording of vital observations is one of the cornerstones of recognising deterioration. However, such observations are often delegated to students and the least experienced staff. This paper explores the teaching and assessment of under-graduate nursing students to recognise and arrange the rescue of a deteriorating patient within the first 16 weeks of their course. The paper describes the development of an integrated Objective Structured Clinical Examination (OSCE) and the subsequent evaluation of this using survey data, student performance results and unobtrusive methods. The results suggest that it is possible to use an integrated OSCE to assess students even at such an early stage in their course. Although data from other Higher Education Institutions in the UK suggests that integrated OSCEs at such an early stage are rare. The appropriate teaching of vital observations, structured hand off and reporting enable students to contribute to safer care and to adhere to the maxim “First Do No Harm”.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".