A Little Uncertainty Goes a Long Way: State and Trait Differences in Uncertainty Interact to Increase Information Seeking but Also Increase Worry
Bibliographic record
Abstract
This study examines the effect of an interaction between intolerance of uncertainty (IU) and situational uncertainty (SU) on worry due to uncertainty and on information seeking. Health providers may benefit from knowing when communicating uncertain information is beneficial. The study was a 2 (IU condition: high vs. low) x 2 (SU condition: high vs. low) experimental design resulting in four conditions to which university students (N = 153) were randomly assigned. IU was manipulated through a linguistic manipulation of responses to an IU questionnaire coupled with written false feedback. SU was manipulated by modifying the information participants read about a fictitious infection. Individuals in the high IU and high SU condition sought the most information and worried most due to uncertainty compared to people in the low IU and low SU condition, who sought the least information and worried least. Findings suggest that high IU may increase positive health behaviors such as screening intentions when individuals are faced with an uncertain health threat, but that it also increases worries due to that uncertainty. Providing opportunities for discussing one's emotional response to uncertainty and providing instrumental support for managing uncertainty (e.g., booking the follow-up appointment) is essential when communicating uncertain information.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".