Content and Quality of Information Provided on Canadian Dementia Websites
Bibliographic record
Abstract
PURPOSE: Information about dementia is important for persons with dementia (PWD) and their caregivers and the Internet has become the key source of health information. We reviewed the content and quality of information provided on Canadian websites for Alzheimer's disease (AD). METHODS: We used the terms "dementia" and "Alzheimer" in Google to identify Canadian dementia websites. The contents of websites were compared to 16 guideline recommendations provided in Canadian Consensus Conference on Diagnosis and Treatment of Dementia. The quality of information provided on websites was evaluated using the DISCERN instrument. The content and quality of information provided on selected websites were then described. RESULTS: Seven websites were identified, three of which provided relatively comprehensive and high-quality information on dementia. Websites frequently provided information about diagnosis of dementia, its natural course, and types of dementia, while other topics were less commonly addressed. The quality of information provided on the websites varied, and many websites had several areas where the quality of information provided was relatively low according to the DISCERN instrument. CONCLUSIONS: There is variation in the content and quality of dementia websites, although some websites provide high-quality and relatively comprehensive information which would serve as a useful resource for PWD, caregivers, and healthcare providers. Improvements in the content and quality of information provided on AD websites would provide PWD and their caregivers with access to better information.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".