O2‐04‐04: Quality and content of online information about the prevention of Alzheimer disease
Bibliographic record
Abstract
Within a generation, over 16 million Americans will suffer from Alzheimer disease (AD) or a related dementia, and the costs for dementia care will reach $1.2 trillion. Faced with this epidemic and fearing the devastating impact of AD on their well-being, older adults are turning to the Internet for health resources: over half of adults aged 65 or over use the Internet, and this figure rises to over three-quarters for adults aged 50-64, with 80% of users searching for health information specifically. Websites hosting AD-related information receive up to several million visitors per month. Despite the popularity of these websites, little is known about quality of these unregulated resources. To address this knowledge gap, we used information-mining techniques to retrieve 308 websites containing information about the prevention of AD and used content analysis to characterize a random subset of the sample (n=102). We assessed the quality of the information using a scoring system based on website characteristics and we quantified the type of advice found on the websites. A panel of physicians evaluated the quality of the advice. We found that a majority of websites (76%) contained at least one indicator of quality (e.g., date, author). Over half of the websites (55%) contained claims supported by specific research studies that could be identified, while 59% of websites contained descriptions of research findings that could not be verified. The overall quality of the information ranged from very poor to excellent. The most common types of advice related to nutrition (95%), exercise (77%), lifestyle (77%), and cognitive stimulation (73%), with specific action items within each category. The presence of quality indicators on a given website did not necessarily predict the quality of the advice itself. Overall, the quality of websites containing information about the prevention of AD is variable, with excellent resources coexisting in the online environment with sources of misinformation. These findings have significant implications for the growing computer-literate older adult population and their health care providers. Further evidence and informed policy are needed to promote the greatest benefits from information available on the Internet.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".