French adaptation and further validation of the word sentence association paradigm to assess hostile attributional biases.
Bibliographic record
Abstract
Hostile attributional biases (HAB) are implicated in several interpersonal and mental health problems. These problems have been shown to be present also in French-speaking areas. However, French-validated assessments of HAB are few and present important limitations that hinder their wide adoption by researchers and health professionals. We therefore developed a French version of the Word-Sentence Association Paradigm – Hostility (WSAP-H), which is a short, easy to administer, and relatively implicit measure of HAB. We then conducted a psychometric study in an online community sample of 315 individuals. Replicating previous validation studies, we found the scores of the French WSAP-H to be internally consistent (α=0.81; ω=0.84), and we provided factorial (one-factor structure), convergent (significant correlations with another HAB measure, as well as with theoretically related constructs of anger and hostility), and discriminant (low or non-significant correlations with negative mood) evidence supporting the validity of WSAP-H scores as measures of HAB. Going beyond previous results, we further showed that these HAB scores 1) demonstrate acceptable test-retest reliability (r=0.77) and stability [non-significant and small (d=0.21) changes at the group level] at eight weeks interval, 2) relate to self-reported interpersonal problems, 3) are distinct from a more general tendency to make negative attributions. The scores of the French WSAP-H thus constitute a reliable and valid measure of HAB, supporting the usefulness of this tool in research and intervention settings.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".