Effect of an educational training intervention on rapid tranquillisation usage – a pilot nursing study in a public hospital in Singapore
Bibliographic record
Abstract
Background: Studies evaluating nursing educational initiatives in rapid tranquillisation procedures are lacking. Objective: This pilot study in a public hospital in Singapore evaluated the effect of an educational training intervention on knowledge and confidence of nurses using rapid tranquillisation in two medical wards. Method: The study design was a pilot pre- and post-test single-group design on a voluntary sample of 75 nurses. The educational training intervention comprised of a 60 min interactive presentation followed by a small-group-based case discussion conducted by an advanced nurse practitioner. Knowledge was measured using a ‘test the knowledge’ questionnaire; a Likert scale measured perceived level of confidence. Results: The knowledge score (overall score = 11) was significantly improved from pre-training (average score: 5.1 (standard deviation, 1.3)) to post-training (average score: 8.1 (standard deviation, 1.8)), t = −12.61, p < 0.001. The participants were more confident after training ( p < 0.001). Conclusion: This study has shown that a classroom training intervention of nurses in rapid tranquillisation procedures improved knowledge and confidence in dealing with patient violence in hospitals and added to the safe practice of rapid tranquillisation. Further studies evaluating the long-term and clinical impact of training with more rigorous study designs are needed to replicate these promising findings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".