Dispositional optimism and optimistic bias: Associations with cessation motivation, confidence, and attitudes.
Bibliographic record
Abstract
OBJECTIVE: To test whether 2 conceptually overlapping constructs, dispositional optimism (generalized positive expectations) and optimistic bias (inaccurately low risk perceptions), may have different implications for smoking treatment engagement. METHOD: Predominantly Black, low-income Southern Community Cohort study smokers (n = 880) self-reported dispositional optimism and pessimism (Life Orientation Test-Revised subscales: 0 = neutral, 12 = high optimism/pessimism), comparative lung cancer risk (Low/Average/High), and information to calculate objective lung cancer risk (Low/Med/High). Perceived risk was categorized as accurate (perceived = objective), optimistically-biased (perceived < objective), or pessimistically-biased (perceived > objective). One-way ANOVAs tested associations between dispositional optimism/pessimism and perceived risk accuracy. Multivariable logistic regressions tested independent associations of optimism/pessimism and perceived risk accuracy with cessation motivation (Low/High), confidence (Low/High), and precision treatment attitudes (Favorable/Unfavorable), controlling for sociodemographics and nicotine dependence. RESULTS: = .029). CONCLUSIONS: Dispositional optimism and lung cancer risk perception accuracy were unrelated. Dispositional optimism was associated with favorable engagement-related outcomes and optimistically-biased risk perception with unfavorable outcomes, reinforcing the distinctiveness of these constructs and their implications for smoking treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".