Evaluation of Work Engagement and Its Determinants in Kermanshah Hospitals Staff in 2013
Bibliographic record
Abstract
OBJECTIVE: Work engagement is a new concept in the field of psychology and human resource management. Increased vitality and enthusiasm is a social phenomenon that brings work engagement for society. This study aimed to evaluate work engagement and its determinants in Kermanshah hospitals' staff. METHODS: This cross-sectional study was conducted on 387 hospital administrative, clinical, paraclinical, and service staff. The sample size was calculated using Krejcei-Morgan table. The data were collected using a questionnaire including demographic characteristics and job engagement components. Then, the data were analyzed using descriptive statistics as well as independent sample t-test and one-way ANOVA. RESULTS: The participants' mean (SD) of age was 32.63±2.7 years and most of them were female (57.6%). The results revealed a significant relationship between work engagement and age group (P=0.01) and work experience (P=0.04). However, no significant relationship was found between work engagement and sex, education level, and job unit. CONCLUSION: The results of this study showed that only job experience and age were associated with work engagement. However, no significant relationship was found between work engagement and education level, sex, and job. Thus, further studies are suggested to investigate the cultural factors and personality traits associated with job enthusiasm among the hospital staff, especially 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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".