Burn-out en bevlogenheid bij basisschooldocenten. Welk effect hebben Job Demands en Job Resources op burn-out en bevlogenheid bij basisschooldocenten?
Bibliographic record
Abstract
Approximately 700.000 people in the Netherlands suffer from burnout. Burnout is characterized by exhaustion, cynicism and lack of personal efficacy. Work engagement is a positive work-related condition. This construct is characterized by vigor, dedication and absorption. In the past, negative aspects of well-being, like burnout, were investigated a lot, especially in service occupations such as teachers. As opposed to negative aspects, positive aspects are taken less into account currently. The literature shows that strengthening work engagement can contribute to primary school teachers in a way that they aren’t quit their job so quickly. The Job Demands-Resources Model takes both negative and positive aspects of well-being into account. Trying to avoid burnout and to stimulate work engagement this study refers to both constructions. The purpose of this study was to analyse the effect of job demands (workload) and job resources (autonomy and social support) on burnout or engagement of elementary school teachers. The data from this study was derived from an online survey research which was conducted by Stichting Consent. Stichting Consent administers all public schools in the communities of Enschede. Cross-sectional design was used to analyse the effect of job demands and job resources on burnout and engagement. 254 primary school teachers’ participated in the survey. More women (n=204) than men (n=50) participated in this study. The results showed that work engagement and burnout correlate negative (r=-0,654). In addition, this analysis presented that job demands and burnout correlate positively. Furthermore, job resources related to work engagement in a positive way. The following regression analysis demonstrated that workload, autonomy and managerial social support had unique predictive value of burnout. Work engagement is only predicted by autonomy. It was also striking that the constructs associated stronger with burnout than with work engagement. Moreover, it appeared that autonomy moderates the relationship between workload and burnout (p<0,037). This study showed clearly, that burnout and work engagement are partially independent constructs. Compared to work engagement, burnout can be predicted more easily by job demands and job resources. Autonomy appeared to play the major role. Also autonomy moderated the relationship between job demands and burnout. These findings imply the importance for following studies/interventions that are aimed to both, reduce burnout and enhance work engagement, to keep autonomy in mind.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.207 | 0.076 |
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".