Bibliographic record
Abstract
Abstract Introduction Falls in the hospital is common. Morse Fall Scale was introduced in 2013 to Hospital Kula Lumpur as a tool for risk assessment in order that prevention strategies could be instituted in accordance to the falls risk. However, the rate of falls was on the rise in the last 5 years despite the use of the tool. The concern was that there is a lack of understanding in the use of the tool or that the scoring was not performed correctly by the nurses. Method A validated structured questionnaire regarding the knowledge of Morse Fall Scale was distributed to the registered nurse of the Medical Department Hospital Kuala Lumpur. Results 209 responses to the questionnaire were collected. The average correct answers were 7 points. Almost a quarter of the nurses had less than 5 correct answers. 50% of this group of nurses has less than 5 years of working experience; most of them only had a diploma and were working in the active medical wards. 17% had full scores, in which 92 % of them had more than 5 years’ experience and had higher nursing qualifications. Overall, the nurses with longer working experience scored 1 point higher than those with a diploma. However, there is no difference in the average score with regards to their place of work, either in clinic or ward. The highest scoring (97.6%) question was the question regarding the purpose of Morse Falls Scale. The least correctly answered (41.6 %) was the question regarding the number of categories in the Morse Falls Scale. Conclusion The nurses with more experience understood the use of the scoring and usage of the Morse Falls Scale better. Generally, with an average score of only 7 out of 10, may reflect an inadequacy in the understanding in falls prevention. References 1. SG Lim, SW Yam. The level of knowledge and competency in the use of the Morse Fall Scale as an assessment tool in the prevention of patient falls, IeJSME, 2016, 10(3): 14-23 2. Cruza S, AL Carvalho P, Barbosa BL. Morse fall scale user’s manual: Quality in supervision and in nursing practice. Procedia - Social and Behavioral Sciences 2015; 171: 334–9. 3. Enein NAE, Ghany ASAE, Zaghloul AA. Knowledge and performance among nurses before and after a training programme on patient falls. Open Journal of Nursing 2012; 2: 358–64.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".