Work Engagement Among Nurses in Turkish Hospitals: Potential Antecedents and Consequences
Bibliographic record
Abstract
This research examined potential antecedents and consequences of work engagement in a sample of nurses employed in hospitals in Turkey..Data were collected from 224 respondents, a 37% percent response rate, using anonymously completed questionnaires.Engagement was assessed by three scales developed by Schaufeli, Salanova, Gonzalez-Roma, and Bakker (2002): Vigor, Dedication and Absorption.Antecedents included personal demographic and work situation characteristics; consequences included measures of work satisfaction, psychological wellbeing, and perceptions of hospital functioning.The following results were observed.First, engagement, particularly dedication, predicted various work outcomes (e.g., job satisfaction, burnout).Second, engagement, particularly vigor, predicted various psychological well-being outcomes but less strongly than these predicted work outcomes.Third, engagement only predicted one aspect of hospital functioning; nurses reporting higher levels of dedication also indicated a higher quality of patient care.Organizations can increase levels of work engagement by creating supportive work experiences (e.g., control, rewards and recognition) consistent with effective human resource management practices .But caution must be exercised before employing North American practices in the Turkish context.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".