Factors affecting motivation of academic staff at nursing faculties
Bibliographic record
Abstract
Objective: This study aims to examine the factors that affecting motivation of academic staff at Faculties of Nursing at Assiut, Sohag and Quena Universities. Methods: A descriptive comparative design was used in the present study. Subjects of the study were all available academic staff who agreed to participate in the study (240). Tool of the study: A self-administered questionnaire was used for data collection it consisted of two parts: The first part included the personal characteristics of academic staff. The second part--Questionnaire of the factors that affecting motivation of academic staff which was adapted from Alam & Farid & Shaheen and colleagues contained 52 items.Results: The findings of the present study showed that the first factor positively motivate the academic staff to teach was self-confidence, followed by choice of teaching staff for their profession. While, the first factor negatively affecting the motivation of the academic staff to teach was anxiety in classroom, followed by examination stress and rewards.Conclusions: The factors positively motivate the academic staff to teach were self-confidence, choice of teaching staff for their profession, and relation of teachers with their colleagues. While, the factors negatively affecting the motivation of the academic staff to teach were anxiety in classroom, examination stress and rewards, socio-economic status of teaching staff, and administration polices. There were statistically significant differences and negative relation between socio-economic status, anxiety in classroom, and academic staff's years of experience while, there were statistically significant differences and positive relation between self-confidence, administrative policies and academic staff's years of experience. Recommendation: The academic staff must be acknowledged for their good performance and should be accompanied with improvement of their salary and academicians should not employ without a professional training by in-service training courses.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".