Assessing hearing and cognition challenges in consumer processing of televised risk information: Validation of self-reported measures using performance indicators
Bibliographic record
Abstract
Public health researchers face important challenges if they wish to include measures of hearing or cognitive ability in risk communication studies. We sought validity evidence for self-report measures of hearing and cognitive ability by comparing those measures to performance-based measures and risk information recall. We measured hearing ability (with audiologist-assisted assessment and self report), cognitive ability (with an established performance task and self report), and reactions to direct-to-consumer prescription drug promotion with adults 18 and older ( n = 1064) in North Carolina, USA, in 2017. We found moderate correspondence between self-reported hearing loss and audiologist-assessed hearing loss. Both measures also showed a small negative association with recall of presented risk information. Cognitive ability results suggested less substantial correspondence between self report and performance task and the measures differed in predicting risk recall. Our results suggested a moderately efficient measure for hearing ability for research on risk information exposure and retention, and yet also suggested the need for caution regarding future use of self-reported cognitive ability as a substitute for a performance-based measure.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".