Predictors of Self-reported Confidence Ratings for Adult Recall of Early Life Sun Exposure
Bibliographic record
Abstract
Use of self-reported confidence ratings may be an efficient method for assessing recall bias. In this exploratory application of the method, the authors examined the relation between case-control status and self-reported confidence ratings. In 2002 and 2003, melanoma cases (n = 141) and controls (n = 143) aged 20-44 years residing in Ontario, Canada, estimated the amounts of time they had spent outdoors in summer activities when they were 6-18 years of age and indicated their confidence in the accuracy of each estimate. The generalized estimating equations extension of logistic regression was used to examine dichotomized confidence ratings (more confident vs. less confident) for activities reported for ages 6-11 years and 12-18 years. Types of activity were associated with more confident reporting for both age strata; as the number of stable outdoor activity periods (total number of similar outdoor periods within each activity) reported by respondents increased, confidence decreased. Cumulative time spent outdoors was also associated with more confidence but reached statistical significance only for the age stratum 12-18 years. There was no statistically significant association between case-control status and self-reported confidence for either age stratum (6-11 years: odds ratio = 0.91; 12-18 years: odds ratio = 1.32), which suggests an absence of recall bias for reported time spent outdoors.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".