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 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 | 0.004 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".