Exploring the causes, symptoms and health consequences of joint and inverse states of work engagement and burnout
Bibliographic record
Abstract
Purpose – The purpose of this research is to explore the relationship between the positive and negative psychological states of work (i.e. engagement and burnout, respectively) and their effects on an individual’s mental and physical health. This study analyzes their separate and joint manifestations. In total, 2,094 nurses were segmented into quadrants that represent a 50/50 median split on both engagement and burnout. The four resulting quadrants were then examined in a series of analyses including logistic regression and ANOVA. Design/methodology/approach – This is a cross-sectional study based on a very large survey (> 2,000 people) in Spain. Data were collected from nurses in collaboration with the official nurses corporations in half a dozen provinces in Spain. Data were analyzed in stages which included zero-item correlations and ANOVA to determine their independence and suitability for predicting states of engagement and burnout. This was followed by a series of binary logistic regression analyses. Findings – The findings suggested that engagement and burnout were generally inversely related (67 per cent of the sample) which is the conventional wisdom in this regard, but 33 per cent of the sample manifested concurrently at either extreme. Burnout was chiefly driven by work demands, as both quadrants of low burnout had lower demands and both quadrants of high burnout had higher demands. Engagement was primarily driven by resources and affinity. Social support acted independently (perhaps as a moderator) by aligning with states of burnout. Worker health was primarily driven by burnout, wherein both states of low burnout exhibited better health and both states of high burnout exhibited poorer health. Originality/value – Much of the current research on this topic considers engagement and burnout to be linear dimensions and focuses on building structural models of the precise relationships between variables. That approach is to be encouraged, but there is also a need to jointly deconstruct dimensions and relationships in a tactile manner that can inform future structural models. The secondary benefit of this approach is that these findings can be submitted directly to managers to provide an easily understood approach for assessments and interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".