Promoting eHealth Literacy in Older Adults: Key Informant Perspectives
Bibliographic record
Abstract
Health literacy has the potential to improve an individual's capacity to access, understand, evaluate, and communicate basic health information and services in order to make appropriate health decisions. We developed a research agenda to help older adults become aware of health literacy and its function in promoting their nutritional health and well-being. A key activity is the development, implementation, and evaluation of an eHealth literacy tool, eSEARCH, targeted at older adults to help improve their eHealth literacy skills. Before consultations were held with this subpopulation to assess their eHealth literacy needs and abilities, key informant interviews were conducted with eight experts in the field of health literacy, the older adult population, and/or online communications. Some experts were identified from the relevant literature; others were identified by informants who had already been interviewed. Informants were asked nine questions about the perceived importance of health literacy in Canada, key considerations in developing an eHealth literacy tool, and supporting resources for advancement of the eHealth literacy tool. Informants agreed that health literacy is a key concept and stressed that key considerations for development of the eSEARCH tool are identifying the target population's needs, focusing on health promotion, and increasing confidence in information-seeking behaviours. Identified challenges are ensuring accessibility, applicability to older adults, and adoption of the tool by dietetic and other health care professionals.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".