Nursing Leadership Style, Training Methods, and Use of Electronic Health Records by Nurses in Jordanian Hospitals: A Descriptive Study
Bibliographic record
Abstract
AIM: The study aimed to assess the level of practice of nursing leadership characteristics during the implementation of electronic health records as perceived by nurses. METHOD: A cross-sectional survey design was used in this study. The study recruited 213 nurses from five hospitals which had recently implemented electronic health solution. Data was collocated using self-administrated questionnaire composed of three sub-domains. The study was granted from the Ethics Committees of the investigators universities and the Jordanian Ministry of Health. Descriptive and correctional statistics were used for data analysis. RESULTS: Data were collected from 213 nurses, the majority of participants (72.3%) were female. Of them, 45% reported receiving full support from their leaders in using electronic health records. Classroom-based training was the most frequently used teaching method during the implementation of electronic health records (59.6%). CONCLUSION: The study demonstrated that diverse leadership styles were practiced during the implementation process of the electronic health records: setting directions, developing people, and redesigning their organizations. The most commonly practiced item was clarifying the reasons for using electronic health records. Such information could enhance the effective adoption of electronic health records by nurses.
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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 | 0.001 | 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) | 0.001 | 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".