Investigating adverse daily life effects following a psychosocial laboratory stress task, and the moderating role of Psychopathology
Bibliographic record
Abstract
Laboratory stress tasks are necessary to closely investigate the stress response in a controlled environment. However, to our knowledge, no study has tested whether participating in such tasks can pose any daily life adverse effect. Fifty-three healthy participants (46 women) took part in a laboratory session where stress was induced using a typical psychosocial stressor: the repeated Montreal Imaging Stress Task (rMIST). Average levels of negative affect (NA), heart rate (HR), root mean square of successive differences (RMSSD), and skin conductance level (SCL), as well as reactivity across all these parameters as measured with the experience sampling method (ESM) in the four days prior to the laboratory session were compared with the four days following the session. We also assessed whether vulnerability to psychopathology moderated these associations. Findings showed that the task did not pose any significant adverse effect on participants. However, there was an unexpected increase in average RMSSD and a decrease in average SCL pre- to post- task. In addition, more vulnerable individuals were more likely to experience an increase in average levels of NA in the days following the task compared to the days preceding it. Our findings suggest that laboratory stress tasks may pose a significant risk to more vulnerable individuals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".