Comparing the Perspectives of Managers and Employees of Teaching Hospitals About Job Motivation
Bibliographic record
Abstract
Recognition of career motivators and understanding of managers and employees in prioritizing them, in order to plan incentives for this understanding, can play an important role in increasing productivity and creating harmony between the goals of the organization and staff. This study was done to survey the importance of career motivating factors from perspective of employees and managers in educational hospitals of Iran. In this study 269 from a total of 1843 employees of educational hospitals in Qazvin province of Iran were selected through Quota-Random sampling and studied along with all 49 Managers. Lawrence Lindale questionnaire with 10 factors where used in order to determine motivational priorities. The results indicated that among the 10 studied motivational factors, from employees' viewpoint; "Good wages", "Good Working Conditions" and "Job Security" have the greatest roles in motivating employees. In the context of perspective agreement amongst employees and managers, the results showed 20 percent agreement. In this study, results of "Independent T" test showed a significant difference in comparison, between prioritizing employees' view and prediction of managers in the factors of "Job Security" (p = 0/031) and "Interesting Work" (p = 0/001). With respect to increase disagreement in the views of managers and employees as compared to previous studies, Managers need to pay more attention to cognition of motivational factors and make their viewpoints closer to actual motivational need of their employees. Attention to this fact can be a great help to the growth and productivity of the organization, making the organizational and individual goals closer and also keeping managers safe from execution of constant and undue motivational patterns.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".