Learning to embrace one’s stress: the selective effects of short videos on youth’s stress mindsets
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Stress is not inherently negative. As youth will inevitably experience stress when facing the various challenges of adolescence, they can benefit from developing a stress-can-be-enhancing mindset rather than learning to fear their stress responses and avoid taking on challenges. We aimed to verify whether a rapid intervention improved stress mindsets and diminished perceived stress and anxiety sensitivity in adolescents. DESIGN AND METHODS: An online experimental design randomly exposed 233 Canadian youths aged 14-17 (83% female) to four videos of the Stress N' Go intervention (how to embrace stress) or to control condition videos (brain facts). Validated questionnaires assessing stress mindsets, perceived stress, and anxiety sensitivity were administered pre- and post-intervention, followed by open-ended questions. RESULTS: The intervention content successfully instilled a stress-can-be-enhancing mindset compared to the control condition. Although Bayes factor analyses showed no main differences in perceived stress or anxiety sensitivity between conditions, a thematic analysis revealed that the intervention helped participants to live better with their stress. CONCLUSIONS: Overall, these results suggest that our intervention can rapidly modify stress mindsets in youth. Future studies are needed to determine whether modifying stress mindsets is sufficient to alter anxiety sensitivity in certain adolescents and contexts.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".