Intolerance of Uncertainty: A Temporary Experimental Induction Procedure
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Intolerance of uncertainty (IU) is a trans-diagnostic construct involved in anxiety and related disorders. Research focused on cross-sectional reporting, manipulating attitudes toward objective and impersonal events or on treatments designed to reduce IU in clinical populations. The current paper presents an experimental procedure for laboratory manipulations of IU and tests mediation hypotheses following the Intolerance of Uncertainty Model. METHODS: On pre-test, undergraduate volunteers (Study 1, n = 43;68% women. Study 2, n = 169;83.8% women) were asked to provide an idiosyncratic future negative life event. State-IU, Worry, Positive and Negative Affect were assessed after that a standardized procedure was used to identify event's potential negative consequences. The same variables were assessed on post-test, after that participants were asked to read-through increasing and decreasing IU statements. RESULTS: Temporary changes on IU were consistently reproduced in both studies. Participants receiving increasing IU instructions reported greater state-IU, Worry and Negative Affect than those receiving decreasing IU instructions. However, this latter condition was not different from a control one (Study 2). Both studies revealed significant indirect effects of IU induction instructions on Worry and Negative Affect through state-IU. LIMITATIONS: Both studies used undergraduate psychology students samples, younger than average population and predominantly female. Experimental manipulation and outcome measures belongs to the same semantic domain, uncertainty, potentially limiting generalizability. CONCLUSIONS: Results supported the feasibility and efficacy of the proposed IU manipulation for non-clinical sample. Findings parallel clinical research showing that state-IU preceded Worry and Negative Affect states.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".