Surviving busy season: Using the job demands‐resources model to investigate coping mechanisms
Bibliographic record
Abstract
Abstract Fatigue and burnout are root causes of audit quality issues and turnover. Leveraging the job demands‐resources theory, we investigate whether two mechanisms can reduce accountants' fatigue and, in turn, improve audit quality. We conduct a field study of public accountants during both normal and busy season work periods, collecting bi‐daily logs to examine whether the use of microbreaks (i.e., brief respite activities) as a job crafting mechanism and/or the receipt of supervisory support as a job resource lessen end‐of‐day fatigue. We posit and find that engaging in microbreaks is associated with reduced end‐of‐day fatigue within busy season. Similarly, we posit and find that higher levels of daily supervisory support during busy season are associated with lower end‐of‐day fatigue. However, neither of these mechanisms is associated with lower end‐of‐day fatigue during normal work periods. Our results also indicate that these two mechanisms function as complements during busy season, with either one significantly reducing end‐of‐day fatigue, but both together having an interactive effect. Further, end‐of‐day fatigue during busy season reduces sleep quality, which increases accountants' fatigue the following morning. In a follow‐up experiment, we consistently find evidence that a 1‐min microbreak reduces fatigue and that this reduction directly translates into improved error detection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".