E-health Literacy and Older Adults: Challenges, Opportunities, and Support Needs
Bibliographic record
Abstract
This study aimed to explore the challenges, opportunities, and support needs related to e-health literacy among older adults. By identifying these key areas, the study seeks to inform the development of targeted interventions and resources to enhance e-health literacy within this demographic. A qualitative research design was employed, involving semi-structured interviews with 16 older adults who have interacted with e-health platforms in the past year. Participants were purposively selected to ensure a diverse range of experiences. Data were analyzed using thematic analysis to identify major and minor themes related to e-health literacy challenges, opportunities, and support needs. The analysis revealed three major themes: Challenges, Opportunities, and Support Needs. Under Challenges, participants identified Technological Barriers, Health Literacy Issues, Accessibility Concerns, and Privacy and Security Fears. Opportunities highlighted were Enhanced Access to Health Information, Improved Patient-Provider Communication, and Personal Health Management. For Support Needs, the study found a demand for Educational Programs, Technical Assistance, and Customizable E-Health Tools. These findings underscore the multifaceted nature of e-health literacy among older adults and the need for comprehensive support mechanisms. Older adults face significant barriers to fully leveraging e-health resources, yet there exist substantial opportunities to enhance their e-health literacy through targeted support and interventions. Addressing the identified challenges and support needs can lead to improved health outcomes for older adults by facilitating more effective use of digital health platforms. The study underscores the importance of developing tailored e-health literacy resources that consider the unique circumstances and preferences of 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".