Development, implementation, and evaluation of a pilot workshop on clinical deterioration using an early warning score
Bibliographic record
Abstract
Background: Early identification of acute clinical decline is critical to improve patient outcomes. Nurses work closely with patients and have an opportunity to identify early signs of clinical deterioration. Unfortunately, signs of clinical deterioration are often missed, resulting in potentially serious adverse events. The National Early Warning Score 2 (NEWS2) is an early warning score (EWS) implemented in the inpatient setting to aid nurses and other health care providers in the recognition of acute decline. No such scoring system exists in the tertiary care centre in St. John’s, Newfoundland. Purpose: To develop and evaluate a multimodal pilot workshop to improve the early recognition of clinical deterioration through introduction of the NEWS2 tool. Methods: An integrative literature review was conducted to identify barriers and facilitators nurses experience when using EWS systems, as well as, to identify educational initiatives and outcomes. Knowles Theory of Andragogy informed the development of the workshop. Informal consultations were completed with key stakeholders of Eastern Health’s medicine program to provide the setting and specific criteria for the workshop. The workshop was delivered to ward nurses, and evaluation surveys were completed. Results: The literature review identified the effectiveness of a multimodal educational workshop for ward nurses. Results from the evaluation survey indicated an overall positive response for both the delivery of the workshop and NEWS2 tool. Conclusion: An evidenced-based pilot workshop was developed that can aid nurses in the early detection of clinical deterioration, and improve patient outcomes. The workshop and evaluation results have been shared with members of Eastern Health with a plan to implement the workshop and NEWS2 tool in the near future.
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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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".