Nurses' Professional Commitment and Its Effect on Patient Safety
Bibliographic record
Abstract
AIMS & OBJECTIVES: The project was designed to assess the level of professional commitment among Jordanian Registered Nurses and examine how professional commitment among nurses relates to patients' safety.BACKGROUND: Professional commitment has received a great deal of interest worldwide. Nurses constitute the largest group of healthcare professionals that spend a majority of their time at the bedside in direct patient care. Nurses have an important role in improving patient safety and providing quality of care.DESIGN: A descriptive, cross-sectional, correlational design was used to answer the research questions.METHODS: A convenience sampling of 180 nurses selected from three accredited hospitals (governmental, private and university-affiliated teaching hospitals) completed two questionnaires; Professional Commitment Questionnaire (PCQ) and a Patient Safety Scale. Descriptive statistics, correlation coefficient, independent sample t-test, and one-way ANOVA test were used in data analysis. RESULT: Nurses' professional commitment was significantly and positively correlated with patient safety. Registered nurses perceived that their level of commitment was medium (M=3.47; SD=1.58 of a 7 point scale), with the highest mean recorded for nurses working in governmental hospitals (M=3.88; SD=1.53). The level of perception of issues related to patient safety was slightly higher than the midpoint (M=5.94; SD=1.38 of a 9 point scale). Nurses' professional commitment was influenced by gender t (158 =-2.33; p =.02), nursing experience in current hospitals (r=- 0.193; p=0.01), current hospital sector (F=4.334, p=0.01), and monthly salary (F=12.327, p=0.000). Patient safety was influenced by nurses' educational level (F=3.306, p=0.03).CONCLUSION: This study provides a preliminary understanding of how professional commitment of registered nurses can enhance patient safety. Managerial support was deemed necessary to enhance nurses' professional commitment, which, in turn, improves healthcare outcomes.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".