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 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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".