Revisiting the SECPT-G: A template for the group-administered socially evaluated cold-pressor test to robustly induce stress
Bibliographic record
Abstract
The Socially Evaluated Cold-Pressor Test (SECPT (Schwabe et al., 2008) [1]; reliably elicits stress responses. We refined the group-administered version of the SECPT (SECPT-G) aiming to increase its' effectiveness. In Experiment 1 (N = 39), we gathered data from 12 participants simultaneously, employing a stress confederate for each participant. In Experiment 2 (N = 69), we gathered data from six participants simultaneously, employing either six stress confederates (individual-observation) or a single one (group-observation). In Experiment 1, we found that the SECPT-G elicited cortisol responses compared to a control condition; in Experiment 2, we replicated these findings and observed that cortisol responses were similar in the individual- and the group-observation setting. The findings of Experiment 2 were corroborated by people's subjective stress experience. Importantly, both experiments show a similar magnitude of cortisol response, and a greater responder rate than in the regular SECPT or the regular Trier Social Stress Test (TSST). The presented SECPT-G template may thus serve as a reliable and efficient stress induction tool that allows standardization across research groups.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".