Improvement Critical Care Patient Safety: Using Nursing Staff Development Strategies, At Saudi Arabia
Bibliographic record
Abstract
Intensive care units (ICUs) provide lifesaving care for the critically ill patients and are associated with significant risks. Moreover complexity of care within ICUs requires that the health care professionals exhibit a trans-disciplinary level of competency to improve patient safety. This study aimed at using staff development strategies through implementing patient safety educational program that may minimize the medical errors and improve patient outcome in hospital. The study was carried out using a quasi experimental design. The settings included the intensive care units at General Mohail Hospital and National Mohail Hospital, King Khalid University, Saudi Arabia. The study was conducted from March to June 2012. A convenience sample of all prevalent nurses at three shifts in the aforementioned settings during the study period was recruited. The program was implemented on 50 staff nurses in different ICUs. Their age ranged between 25-40 years. Statistically significant relation was revealed between safety climate and job satisfaction among nurses in the study sample (p=0.001). The years of experiences in ICU ranged between one year 11 (16.4) to 10 years 20 (29.8), most of them (68%) were working in variable shift, while 32% were day shift only. Improvements were observed in safety climate, teamwork climate, and nurse turnover rates on ICUs after implementing a safety program. On the heels of this improvement; nurses' total knowledge, skills and attitude were enhanced regarding patient safety dimensions. Continuous educational program for ICUs nursing staff through organized in-service training is needed to increase their knowledge and skills about the importance of improving patient safety measure. Emphasizing on effective collaborative system also will improve patient safety measures in ICUS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".