Investigating the Impact of Stress on Pain: A Scoping Review on Sense of Control, Social-Evaluative Threat, Unpredictability, and Novelty (STUN Model)
Bibliographic record
Abstract
Background: Stress can have paradoxical effects on pain, namely hyperalgesia and hypoalgesia. Four situational characteristics activate the hypothalamic-pituitary-adrenal axis, leading to a physiological stress response: lacking Sense of control, social-evaluative Threat, Unpredictability and Novelty (STUN). This scoping review reports on the types of evidence published on the effects of STUN characteristics on pain outcomes. Databases/Data Treatment: Searches of primary electronic databases were performed to identify articles published on adults between 1990 and 2021 that contained search terms on pain and stress/STUN characteristics. A total of 329 articles were included in the analysis. Results: Only 3.3% of studies examined simultaneously >1 STUN component. Almost all observational studies (177/180) examined the association between perceived stress and pain without measuring physiological stress responses. Of the 130 experimental studies, 78 (60.0%) manipulated stressful characteristics through nociception, and only 38.5% assessed if/how stress manipulation impacted perceived stress. Conclusion: There is a clear lack of integration of the characteristics that trigger a physiological stress response in the pain field. Only 3.3% of studies examined simultaneously more than one STUN component and there is an unequal attention given to individual components of the STUN framework. Recommendations for future research include selection of stress manipulations/measurements that are more precisely inducing/reflecting neurobiological mechanisms of stress responses to insure valid integration of scientific knowledge.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".