Can't stress this enough: can biofeedback increase the use of stress interventions?
Bibliographic record
Abstract
Undergraduate students experience many stressors throughout their education. An abundance of stress coping methods exists to help students cope; however, many require a significant time investment (e.g., exercise, meditation). Some quick stress coping methods (e.g., deep breathing, cognitive reappraisal) are effective for coping with in-the-moment stressful situations, but students rarely use these coping methods. It is unclear whether this lack of use is due to lack of knowledge, lack of belief that the strategy is useful, or other factors. Our study examined the first two ideas by introducing deep breathing and cognitive reappraisal to the participants with and without biofeedback. We compared the effectiveness of a physiological technique (deep breathing) to a cognitive technique (cognitive reappraisal). Contrary to our hypotheses, coping strategy and biofeedback did not increase the use of either coping strategy throughout the semester; however, participants across all groups reported using deep breathing and cognitive reappraisal more in Part 2 than Part 1. Aligning with our hypothesis, deep breathing, and cognitive reappraisal as a stress coping strategies lead to similar changes in our biofeedback measure and seem to lead to better mental control over the participant’s stress reaction.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".