USER EXPERIENCES OF OLDER ADULTS NAVIGATING AN ONLINE DATABASE OF COMMUNITY-BASED PHYSICAL ACTIVITY PROGRAMS
Bibliographic record
Abstract
Abstract As cataloguing health care services becomes more internet-based, older adults are increasingly expected to navigate services online. Previous studies have outlined the needs, barriers, and facilitators of older adults using eHealth, however, none included user experiences in navigating websites. Connecting community-living older adults with local, high quality, community-based physical activity programs is a gap in efforts to promote physical activity by older adults. This study aimed to: 1) explore older adult user experiences navigating an online health database of local physical activity programs; 2) compare navigational feedback with age-friendly website design guidelines; and 3) assess online database completeness. Focus groups, including guided navigation tasks and a semi-structured interview script, gathered user experiences of fifteen older adults (≥65). A literature review produced three age-friendly best practice website design guidelines. A website search for local physical activity programs was completed. The design of the online database website was challenging for older adult participants to navigate and was not ’intuitive’. In navigating the online database, the older adults identified multiple discrepancies with established guidelines for designing age-friendly websites. A total of 187 local physical activity programs were missing from the database. Findings provide novel insight into user experiences of older adults navigating online health and physical activity program websites. Redesign following age-friendly website recommendations would empower older adults in use of online databases and promote awareness of local physical activity programs. Health care providers need reliable and age-friendly online resources to link their patients with local physical activity programs to promote healthy aging.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".