Health Promotion and Access to Online Health Information among Older Adults
Bibliographic record
Abstract
2204 The Internet has grown at a tremendous rate in the last decade and it has offered consumers unparalleled opportunities to acquire health information. However, questions remain about access to the Internet among certain segments of the population such as older adults and the proclivity to use this medium in health promotion arena. PURPOSE: The present study examined Internet use and access to health information online among older men and women using data from nationally representative Canadian Household Internet Use survey. MEASURES: Secondary analysis included a sample of 7,631 individuals over 65 years of age. In the sample, 61% were men, 51% were married or in common-law relationship, 57% had less than high school education, 93% were retired, and 50% had a yearly income of less than or equal to the Statistics Canada's Low-Income Cut-Off. Demographic and Internet use information was elicited using an interviewer administered questionnaire. RESULTS: Among the older adults, only 10% had reported using the Internet. Approximately 20% of those used the medium daily. Most internet users accessed the Internet from their home (72%) using telephone line (99%) and 82% spent less than 49 hours each month on line. Among the Internet users, 58% used it to access health information online. This represents approximately 5% of the older adults involved in the survey. A negative relationship between age and access of health information online was observed. Male gender, being married or in coupled relationships, higher levels of education and income, access to Internet from multiple locations other than one's home, and frequent use of the Internet (daily) increased the likelihood of individuals using health information online. CONCLUSION: This present study shows that older adults are on the sidelines when it comes to using the Internet and accessing health information online. Studies have shown that older adults are willing to access and use health information successfully, if provided proper training and support. The present study highlights the need for such training and support for seniors before the Internet is used as a medium for health promotion. Implications of the findings for health promotion and health care delivery in the future will be discussed. Supported by Acadia University Research Fund
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".