Benefiting From Digital Use: Prospective Association of Internet Use With Knowledge and Preventive Behaviors Related to Alzheimer Disease in the Israeli Survey of Aging
Bibliographic record
Abstract
BACKGROUND: Previous work documented the beneficial association between internet use and improved cognition, functional capacity, and less cognitive decline among people in late adulthood. This work focused on potential mechanisms of such an association: knowledge on Alzheimer disease (AD) and preventive behaviors related to AD. OBJECTIVE: The aim of this study was to examine prospective associations of internet use and perceived computer skills with knowledge on AD and preventive behaviors related to AD. METHODS: The sample included 1232 older adults (mean age 71.12 [SD 9.07]) drawn from the Israeli branch of the Survey of Health, Aging, and Retirement in Europe (SHARE-Israel). The sample is representative of Israeli households of adults aged 50 or older and their spouses. Data analyzed were collected in person during 2015 (Wave 6), and in a drop-off questionnaire following the in-person 2017 data collection (Wave 7). RESULTS: Although both internet use and perceived computer skills were prospectively associated with knowledge and behaviors related to AD in bivariate analyses, after controlling for sociodemographics, only internet use was associated with more such knowledge (β=.13, P<.001) and behaviors (β=.22, P<.001). CONCLUSIONS: Internet use emerged as a prospective predictor of protective factors against AD. Policymakers should advance digital engagement so as to enhance knowledge on AD and preventive behaviors among 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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".