Is all validation equal? Evaluating sensory- and emotion-focused validation in the context of experimentally induced pain
Bibliographic record
Abstract
Validation has been examined in experimental and clinical settings, but examination of whether specific content of validation responses affect pain-related outcomes has not been considered. We examined the impact of sensory- or emotion-focused validation following a pain task. Participants ( N = 140) were randomly assigned to one of three validation conditions (i.e. sensory, emotional, or neutral) and completed the cold pressor task (CPT). Participants provided self-report ratings of pain and affective-related variables. Subsequently, a researcher validated emotional, sensory, or no aspects of participants’ experience. The CPT was repeated, as were the self-report ratings. No significant differences were observed across conditions in pain or affective outcomes. All conditions reported an increase in pain intensity and pain unpleasantness across CPT trials. These findings suggest validation content may not impact pain outcomes during painful experiences. Future directions to understanding the nuances of validation across interactions and settings are discussed.
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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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".