Factor Predicting the Use of Physical Restrain in Clinical Setting
Bibliographic record
Abstract
PURPOSE: The purpose of the study is to identify the factors predicting psychiatric nurses’ decision to use physical restraint in a clinical psychiatric setting in the Province of Jeddah, Saudi Arabia. METHODS: A descriptive explanatory design was used. 110 nurses working in a psychiatric hospital in Jeddah city were recruited during the period 27th April to 29th June 2017. The outcome was assessed on the following scales: level of knowledge, occupational stress, working environment, nurses’ attitude toward use of physical restraint and nurses’ practice toward use of physical restraint. RESULTS: The study participants had sufficient knowledge about the use of physical restraint, experienced high levels of occupational stress, suffered an unproductive working environment and accepted attitudes and practice toward physical restraint. In addition, these variables significantly predicted the nurses’ use of physical restraint. CONCLUSION: The findings revealed that the level of knowledge and occupational stress scales, the working environment, and nurses’ attitude and practice toward the use of physical restraint significantly predicted the nurses’ use of physical restraint. RECOMMENDATIONS: The study recommends the establishment of educational and awareness programmes for nurses to better understand the concept of restraining a patient and the consideration of alternative measures for controlling agitated and violent patients. It also recommends that providing adequate staffing and other resources, maintaining a therapeutic ward environment, and decreasing work-related stress could influence psychiatric nurses’ decisions to use physical restraint on their patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".