Quality improvement in hospital: How knowledge and attitude affect job performance across different professional groups
Bibliographic record
Abstract
Objective: Improving the performance of healthcare organizations is a major concern within health systems. This study aims to explore the relationship between hospital staff’s knowledge and attitudes about continuous quality improvement (CQI) and their perceived job performance while determining if professional groups moderate this relationship.Methods: A total of 250 questionnaires were distributed among three main job groups at a public hospital in Iran. Statistical analysis included variance-based structural equation modeling and Pearson correlation coefficients.Results: Of the 250 distributed questionnaires, 196 were returned (response rate: 78%). The path coefficient between staff knowledge and performance was 0.390 higher in the physician group than in the non-physician group, and 0.207 higher in the administrative-financial group. The path coefficient for the non-physician group was 0.120 higher than that of the administrativefinancial group. For staff attitudes and performance, the path coefficient was 0.160 higher in the physician group than in the non-physician group, and 0.090 higher than in the administrative-financial group. The administrative-financial group had a 0.070 higher path coefficient than the non-physician group.Conclusions: The study indicates positive relationships between hospital employees’ knowledge and attitudes about quality improvement and their job performance. These relationships were not significantly moderated by professional groups.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".