The correction of health misconceptions in older adults with cognitive impairment
Bibliographic record
Abstract
Abstract Background The use of a refutational technique (e.g., “myths” versus “facts”) to correct health‐related misconceptions is ubiquitous, yet little work has addressed whether older adults’ cognitive status influences misconception correction. We examined belief in COVID‐19 misconceptions in older individuals with and without cognitive impairment, and whether a simple refutational technique would be effective at reducing misconception belief in these populations. Method Older adults (N = 62, 36 Males) took a pretest of 10 COVID misconceptions (from the CDC) over the telephone. Participants received immediate corrective feedback (i.e., the correct answer and an explanation of the correct answer). Individuals took a surprise retest after 25 minutes and again one week later. Cognitive status was assessed with a telephone‐administered neuropsychological battery and Montreal Cognitive Assessment‐Blind (MoCA‐Blind). Cognitive status groups were compared on misconception belief and corrections. Exploratory analysis correlated corrections with performance on the neuropsychological battery and demographics. Results Older adults (M = 75.26 years, SD = 6.61) were split into a cognitively impaired group (N = 28) and an unimpaired group (N = 34) based on recommended cutoffs for the MoCA‐Blind; these groups did not differ in terms of age or education. Impaired older adults endorsed more misconceptions on the pretest than unimpaired older adults, t(60) = 3.61, p < .001, d = .92. Importantly, both groups made equivalent corrections from the pretest to the first retest (p = .439, d = .21) and maintained those corrections across the one week delay (p = .907, d = .03). Only memory errors on the recognition portion of the neuropsychological battery were associated with performance gains (r = ‐ .33, p < .01). Individuals who made more false alarms (i.e., incorrectly recognizing a word as having been presented) were less likely to correct misconceptions. Conclusion A refutational technique can be effectively used to correct health‐related misconceptions in even older adults who are cognitively impaired. Moreover, this easily‐implementable technique was effective for older adults of all ages and education levels. Rather, recognition memory errors alone were important in the correction of misconceptions.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".