Burnout, Job Characteristics, and Intent to Leave: Does Work Experience Have Any Effect
Bibliographic record
Abstract
Recent changes in employment conditions due to globalization, innovation, and economic crisis have made many employees vulnerable to job burnout. The literature shows that innovation can have both positive and negative impacts on employees' well-being. The implementation of new technology may increase job demand, employees feeling of emotional exhaustion, and intentions of leaving. This study examines if the number of years of work experience may moderate the level of burnout felt by employees (i.e., emotional exhaustion, depersonalization, and personal accomplishment), their perceptions of job characteristics (i.e., job demand, decisional latitude, and social support) and their intent to leave. Data are collected among agents and advisors working at a Canadian high education institution, a few months after the implementation of Banner, an information system used to manage and query students' data. The results show no significant effect of work experience on burnout and job characteristics, but a positive significant effect on intent to leave. Experienced employees are not more prepared than their less experienced colleagues. The findings suggest that employers should increase job resources (e.g., training, feedback, and social support at work) during the implementation of new technology.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".