Negative Interpretation Bias and the Experience of Pain in Adolescents
Bibliographic record
Abstract
UNLABELLED: Negative interpretation bias, the tendency to appraise ambiguous situations in a negative or threatening way, has been suggested to be important for the development of adult chronic pain. To our knowledge, this is the first study to examine the role of a negative interpretation bias in adolescent pain. We first developed and piloted a novel task that measures the tendency for adolescents to interpret ambiguous situations as indicative of pain and bodily threat. Using this task in a separate community sample of adolescents (N = 115), we then found that adolescents who catastrophize about pain, as well as those who reported more pain issues in the preceding 3 months, were more likely to endorse negative interpretations, and less likely to endorse benign interpretations, of ambiguous situations. This interpretation pattern was not, however, specific for situations regarding pain and bodily threat, but generalized across social situations as well. We also found that a negative interpretation bias, specifically in ambiguous situations that could indicate pain and bodily threat, mediated the association between pain catastrophizing and recent pain experiences. Findings may support one potential cognitive mechanism explaining why adolescents who catastrophize about pain often report more pain. PERSPECTIVE: This article presents a new adolescent measure of interpretation bias. We found that the tendency to interpret ambiguous situations as indicative of pain and bodily threat may be one potential cognitive mechanism explaining why adolescents who catastrophize about pain report more pain, thus indicating a potential novel intervention target.
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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 | 0.015 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".