Examining burnout in employed university students
Bibliographic record
Abstract
Purpose Burnout has been studied by organizational researchers for nearly 50 years (Maslach and Schaufeli, 2017; Schaufeli et al. , 2009); however, little attention is given to burnout experienced by employed students who may be prone to the symptoms of burnout as they juggle multiple demanding roles. Burnout in employed students has previously been conceptualized as a bi-factor model consisting of three dimensions: general burnout, apathy and exhaustion (see Rauti et al., 2019 for further information). The purpose of this paper is to develop and validate a novel and theoretically driven tool to assess burnout in employed students. Design/methodology/approach A sample of 239 employed undergraduate students from a university in southwestern Ontario completed an online survey which included the University of Windsor Employed Student Burnout Survey. Participants also completed six additional measures for scale validation purposes. Findings Confirmatory factor analysis supported a four-factor model of the employed student burnout scale: apathy toward employment, exhaustion toward employment, apathy toward academics and exhaustion toward academics. The findings also supported a bi-factor version of the four-factor model. Correlation analyses provided evidence for convergent and divergent validity. Originality/value The experience of burnout for employed students is unique as employed students balance the demands of work and school simultaneously. This research suggests that experiences of burnout from work and burnout from school may be distinct from one another and that burnout is context specific.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".