Development and Initial Validation of the White Bear Suppression Inventory (Smoking Version)
Bibliographic record
Abstract
INTRODUCTION: Thought suppression can produce a paradoxical rebound in unwanted cognition. Although interest in the role of suppression in nicotine dependence is growing quickly, a validated measure specifically assessing suppression of smoking-related thoughts does not exist. The present study describes the development of the White Bear Suppression Inventory-Smoking Version (WBSI-S). METHOD: The WBSI-S, in vivo monitoring of avoidance, and several other measures were completed as a part of a larger study on smoking cessation. Participants (N = 172) completed measures either during (n = 83) or preceding a smoking cessation attempt. RESULTS: Factor analysis revealed a two-factor structure for the WBSI-S, which was consistent across experimental groups. Both the Intrusive Smoking-related Thoughts and Thought Suppression subscales showed strong internal consistency. The Suppression subscale showed good convergent and discriminant validity; the Intrusion subscale demonstrated equivocal discrimination from other constructs. Participants completing the measure during a quit attempt reported higher self-reported suppression of thoughts about smoking than did continuing smokers. CONCLUSIONS: Overall, results support the construct validity of the suppression subscale and emphasize the importance of assessing suppression independently from intrusion.
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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 | 0.006 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".