Selective attention as a protagonist in contemporary workplace stress: implications for the interruption age
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The ubiquity of instant messages and email notifications in contemporary work environments has opened a Pandora's Box. This box is filled with countless interruptions coming from laptops, smartphones, and other devices, all of which constantly call for employees' attention. In this interruption era, workplace stress is a pervasive problem. To examine this problem, the present study hypothesizes that the three-way interaction among the frequency with which interrupting stimuli appear, their salience, and employees' deficits in inhibiting attentional responses to them impacts mental workload perceptions, ultimately leading to stress. The study, further, probes a related form of self-efficacy as a potential suppressor of interruption-based stress. DESIGN: The study used a 2 (low vs. high frequency) × 2 (low vs. high salience) mixed model design. METHODS: The 128 subjects completed a test of their inhibitory deficits and rated their mental workload perceptions and experiences of stress following a computer-based task. RESULTS: Inhibitory deficits and increased interruption salience can alter the perception of mental workload in contemporary work environments for the worse, but interruption self-efficacy can help offset any resulting interruption-based stress. CONCLUSIONS: This study extends the literatures on work interruptions as well as on stress and coping in the workplace.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".