Evaluating the Effective Factors in Motivating Nurses to Participate in On-the-job Training Courses
Bibliographic record
Abstract
Background: On-the-job Training is one of the most useful and economical methods for nurses to keep up with the latest progress in technology, as well as medical and social sciences. Encouraging nurses to improve their knowledge and skills is one of the most important responsibilities of a nursing management. This study aims to evaluate the effective factors in Motivating Nurses to Attend On-the-job Training Courses.Methods: This is a descriptive-analytical study which is on the basis of existing facts and information about the subject of the study. Our population comprises of 147 qualified nurses working in the hospitals of Torbat Heidariye University of Medical Sciences. Data was collected using questionnaire and analyzed using SPSS 21.Results: 46.3% of nurses were in 30-40 age group, 54.2% were females, 81.2% were married, 86.4% had B.S. 58.6% of nurses have been working in the hospital for less than 5 years. 87.8% of nurses were working on shifts, and the rest (1.8%) were supervisors. 94% of nurses agreed on the necessity of on-the-job training (moderately or highly required).Conclusion: we found out that there is no significant relationship between motivating factors and demographic characteristics. Also, there is an important difference between genders and organizational motivating factors. It means that the rate of male nurses’ participation in on-the-job training courses is higher than that of female nurses.Keywords: motivation, nurse, on-the-job training, hospital
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".