Internet Search Results for Older Adult Physical Activity Guidelines: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Older adults seek health-related information through casual internet searches. Yet, researchers focus on peer-reviewed journals and conference presentations as primary dissemination strategies. Representatives of mass media are alerted (passive diffusion) of new studies or recommendations, but the veracity of the information shared is not often analyzed, and when it is, the analysis is often not comprehensive. However, most older adults do not have access to peer-reviewed journal articles or paid subscription services for more reputable media outlets. OBJECTIVE: We aimed to determine what information was readily available (ie, open access) to older adults who may casually search the internet for physical activity recommendations. METHODS: We performed a 6-part scoping review to determine the research question and available evidence, and extract data within open-access top hits using popular online search engines. Results were categorized by a dissemination model that has categories of sources, channels, audience, and messages. RESULTS: After the iterative search process, 92 unique articles were included and coded. Only 5 (5%) cited physical activity guidelines, and most were coded as promoting healthy aging (82/92, 89%) and positive framing (84/92, 91%). Most articles were posed as educational, but the authors' credentials were rarely reported (ie, 22% of the time). Muscle strengthening and balance components of the physical activity guidelines for older adults were rarely reported (72/92, 78% and 80/92, 87%, respectively) or inaccurately reported (3/92, 3% and 3/92, 3%, respectively). CONCLUSIONS: Inconsistent messages lead to mistrust of science and public health representatives. This work highlights the lack of evidence within existing open-access resources. Further efforts are needed to ensure evidence-based public health messages are in the sources and channels older adults are using to inform their knowledge and behaviors.
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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 | 0.050 | 0.211 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.070 | 0.065 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".