Factors Affecting Job Motivation among Health Workers: A Study From Iran
Bibliographic record
Abstract
OBJECTIVE: Human resources are the most vital resource of any organizations which determine how other resources are used to accomplish organizational goals. This research aimed to identity factors affecting health workers' motivation in Shahid Beheshti University of Medical Sciences (SBUMS). METHOD: This is a cross-sectional survey conducted with participation of 212 health workers of Tehran health centers in November and December 2011. The data collection tool was a researcher-developed questionnaire that included 17 motivating factors and 6 demotivating factors and 8 questions to assess the current status of some factors. Validity and reliability of the tool were confirmed. Data were analyzed with descriptive and analytical statistical tests. RESULTS: The main motivating factors for health workers were good management, supervisors and managers' support and good working relationship with colleagues. On the other hand, unfair treatment, poor management and lack of appreciation were the main demotivating factors. Furthermore, 47.2% of health workers believed that existing schemes for supervision were unhelpful in improving their performance. CONCLUSIONS: Strengthening management capacities in health services can increase job motivation and improve health workers' performance. The findings suggests that special attention should be paid to some aspects such as management competencies, social support in the workplace, treating employees fairly and performance management practices, especially supervision and performance appraisal.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".