Identifying the most effective acute stress induction methods for producing SAM- and HPA-related physiological responses: a meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Laboratory-based stress inductions are commonly used to elicit acute stress but vary widely in their procedures and effectiveness. We compared the effects of stress induction techniques on measures of two major biological stress systems: the early sympathetic-adrenal-medullary (SAM) and the delayed hypothalamic-pituitary-adrenal (HPA) axis response. DESIGN: A review and meta-analysis to examine the relationship between stress induction techniques on cardiorespiratory and salivary measures of SAM and HPA system activity. METHODS: A systematic literature search identified 245 reports and 700 effects. RESULTS: = 0.37) responses. Three factors moderated these associations: the stress system examined (SAM vs HPA), the specific stress induction technique employed (e.g., Cold Pressor), the physiological sampling time relative to the stress induction, and participant sex. Loud music elicited the most robust SAM-related effects, whereas combined stress inductions elicited the most robust HPA-related effects. Men showed stronger stress responses than women. CONCLUSIONS: Stress induction techniques variably elicit SAM - and HPA-related responses. Results recommend specific induction techniques for targeting stress systems, highlighting the importance of carefully selecting methodologies in laboratory 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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".