An Adapted Word-Sentence Association Paradigm for Generalized Anxiety and Worry: Assessing Interpretation Bias
Bibliographic record
Abstract
Individuals with pathological worry, a common symptom of Generalized Anxiety Disorder (GAD), make threatening interpretations of ambiguous information related to various life domains (e.g., finances, relationships). A word-sentence association paradigm (WSAP) computer task, originally designed for social anxiety, was adapted to assess two threat-related interpretation biases common among individuals with generalized anxiety and pathological worry. The two biases, which have yet to be investigated simultaneously, include: accepting threatening interpretations and rejecting benign interpretations of ambiguous information (for the original WSAP, see Beard & Amir, 2009). It was hypothesized that endorsing threat interpretations on the WSAP would be associated with greater bias for threat on a validated self-report measure of bias, and would predict GAD symptoms and worry after trait anxiety and depression were statistically controlled. Results from a non-clinical sample (N = 148) provided support for the convergent validity of the WSAP. After controlling for trait anxiety and depression, a bias to accept threat interpretations predicted a unique and significant proportion of variance in measures of GAD symptoms and worry. A bias away from non-threat (i.e., rejecting benign interpretations) was unrelated. The WSAP shows evidence of sensitivity and specificity to GAD symptoms and worry, and appears to be a unique and specific measure of two types of threat bias making it theoretically informative and clinically useful.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".