Bibliographic record
Abstract
Our society is preoccupied with stress. Previous research demonstrates that the majority of the sampled population perceives the consequences of stress to be primarily negative. By reframing the consequences of stress to highlight both positive and negative effects of stress, individuals may experience more efficient and adaptive stress responses (Liu et al., 2017). The extent to which we can reframe the consequences of stress to elicit more adaptive responses thus merits further investigation. This dissertation investigated the effects of reframing (positive, negative, balanced, and control) on stress responsivity, while also priming individuals on personal strengths (resilient strengths or non-resilient/control strength) to further enhance coping. Through two studies, the current dissertation examined whether different information presented on the consequences of stress via reframing contributes to improved responsivity to stressors, and whether priming an individual to believe in select strengths further enhances receptivity to reframing and reactivity to stressors. Outcome measures included both objective, physiological indexes of stress (heart rate, blood pressure, and electrodermal activity), test performance on stressor-task, and subjective ratings of stress (self-report responses via visual analogue scales). Mixed-ANOVA, linear regression, and exploratory hierarchical modelling were used to analyze the data. Results via visual analogue scales support the efficacy of balanced reframing in reducing self-reported stress, and provide some evidence for its efficacy across physiological parameters of stress via electrodermal activity. Across measures, results provide little support for the efficacy of strength priming in eliciting more adaptive responsivity to stressors. However, balanced framing and resilient strength priming may interact to reduce perceptions of stress as threatening and uncontrollable. Taken together, findings across two studies suggest that reframing stress by presenting both positive and negative information on the outcomes of stress may be an important step in the education of stress to better manage everyday stressors. Further, efforts to personalize this intervention approach by tailoring it to individual may be an area worthy of future research. Key Words: Beliefs; Coping; Personal Strengths; Reframing; Responsivity; Stress
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".