Sense of coherence and work-related patterns of behavior and experience among faculty nurse educators in Egypt
Bibliographic record
Abstract
Background: Nursing education is associated with high intensity work and potential burnout. Sense of coherence mitigates the effect of job stress and enhances work wellness. This study aims to identify work-related patterns of behavior and experience among nurse educators and examine the independent relationship between sense of coherence and workplace behavior patterns.Methods: The study used a cross-sectional descriptive design and a sample of 130 nurse educators teaching in the Faculty of Nursing, Alexandria University, Egypt. The nurse educators completed questionnaires measuring sense of coherence and work-related patterns of behavior and experience.Results: Discriminant analysis delineated four work behavior patterns: healthy (16.2%), frugal (1.5%), overburdened (68.5%), and burnout (13.8%). Sense of coherence was positively associated with the healthy work pattern (r = .66, p < .001), and negatively associated with the burnout (r = -.63, p < .001) and overburdened (r = -.18, p < .05) patterns. Regression analyses, controlling for years of teaching experience, indicated that a weak sense of coherence was associated with classification of nurse educators into the overburdened work pattern (B = -.05, SE = .12, Wald χ2 = 13.13, p < .001) and the burnout work pattern (B = -.10, SE = .02, Wald χ2 = 21.52, p < .001) compared to healthy work pattern.}Conclusions: The study findings highlight the importance of sense of coherence as a health-promoting resource in the workplace. Strategies are discussed for creating meaningful work experiences to reinforce a sense of coherence and simultaneously cultivate work-related wellness among nurse educators.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".