Impact of Ecological Momentary Interventions on Regulatory Strategies of Perceived Stress at Work: An Exploratory Study Based on the Application "MON SHERPA” Used in an Ecological Context
Bibliographic record
Abstract
Based on ICT, specifically smartphones and their mobile apps, this exploratory study questions the impact of EMIs on employees' perceived stress during workdays. A sample of 15 workers, working at least 3 days a week and divided into one control group (n=5) and one experimental group (n=10), used an EMI application called "Mon Sherpa" for one week. Participants responded to two questionnaires at the beginning of the study: a sociodemographic questionnaire and the PSM-9 (Psychological Stress Measure). They completed the PSM-9 once again in the middle and at the end of the experiment to compare the score's evolution depending on the formed groups. Additionally, semi-structured interviews were conducted with participants of the experimental group (n=9) to identify their perception of the application. The statistical results indicated no effects of the EMIs. However, interviews indicated somatic, behavioral, and cognitive evolution throughout the experiment in the field of stress, anxiety, and invasive thoughts. These conflicting results might be explained by an immediate but not lasting effect of EMIs on work-related stress. It may also be partly explained by some limitations of the study. More cross-disciplinary and larger research is required.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".