What makes people anxious about pain? How personality and perception combine to determine pain anxiety responses in clinical and non-clinical populations
Bibliographic record
Abstract
Although anxiety has both dispositional and situational determinants, little is known about how individuals' anxiety-related sensitivities and their expectations about stressful events actually combine to determine anxiety. This research used Information Integration Theory and Functional Measurement to assess how participants' physical concerns sensitivity (PCS) and event expectancy are cognitively integrated to determine their anxiety about physical pain. Two studies were conducted - one with university students and other with anxiety clinic patients - in which participants were presented with multiple scenarios of a physically painful event, each representing a different degree of event probability from which subjective expectancies were derived. Independent variables included PCS (low, moderate, and high) and event expectancy (low-, medium-, high-, and non-probability information). Participants were asked to indicate their projected anxiety (dependent measure) in each expectancy condition in this 3 × 4 mixed, quasi-experimental design. The results of both studies strongly suggest that PCS and event expectancy are integrated additively to produce these pain anxiety scores. Additional results and their implications for the treatment of anxiety-related disorders are also 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".