Knowledge About COVID-19 Symptoms, Transmission, and Prevention: The Relationship With Cognitive Status in Older Adults
Bibliographic record
Abstract
Objective: Advanced age poses an increased risk for cognitive impairment, and therefore, poor knowledge regarding the risks associated with COVID-19 may confer vulnerability. We administered a COVID-19 Knowledge Questionnaire to older persons to evaluate the association between knowledge regarding public health recommendations, and cognitive status as measured by the Montreal Cognitive Assessment (MoCA). Method: Ninety-nine participants completed a 22-item questionnaire about COVID-19 symptoms, risks, and protective strategies, and they also completed the MoCA. Associations between knowledge and cognitive status were examined via Spearman correlations. Results: The mean (SD) age of participants was 72.6 (7.6) years, and MoCA scores averaged 23.4 (4.5) points. Higher MoCA total scores were significantly ( p < .001) correlated with a greater number of correct questionnaire responses. Higher Orientation and Memory Index scores were moderately associated with an increased number of correct responses ( p < .001), with the Executive Index exhibiting a significant albeit weaker association. MoCA Index scores assessing attention, language, and visuospatial functioning were not significantly associated with COVID-19 knowledge. Conclusions: Given the rapid transmission rate of the SARS CoV-2 infections, COVID knowledge lapses will likely have deleterious repercussions. Public health messages should ensure effective acquisition and retention of COVID specific information, especially in cognitively compromised older adults.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".