Reframing the individual stress response: Balancing our knowledge of stress to improve responsivity to stressors
Bibliographic record
Abstract
Although responses to stressors have both adverse and positive consequences on health, many believe that stress is entirely negative. Research revealed that negative beliefs about stress can hinder well-being and result in the avoidance of stressors. Stress-optimizing interventions that target various stress appraisal processes may be a useful tool to reframe how individuals understand and respond to stressors. The current study extends previous findings on stress reframing and sought to examine the extent to which the presentation of information about stress outcomes may influence the individual to respond to subsequent stressors. Seventy-seven undergraduate students (96% female) were randomized into one of four reframing conditions (balanced stress outcomes, negative stress outcomes, positive stress outcomes, and control) and underwent a psychosocial stressor. Results highlight similarities between balanced and positive framings of stress across measures of heart rate and blood pressure, whereas subjective ratings of stress and electrodermal activity suggest balanced framing may be efficacious in attenuating stress. Findings are discussed in the context of differing stress-optimizing interventions and consider the complexities of the individual stress response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".