Where do older Australians receive their health information? Health information sources and their perceived reliability
Bibliographic record
Abstract
Background: Chronic disease prevalence is increasing, in part due to the ageing population, adding further pressure to Australia’s over-stretched primary health care services. While patients are encouraged to self-manage their chronic disease(s) in order to minimise the impact on their day-to-day functioning, little is known about where older adults receive health information and their perceptions of the reliability of these sources. Such knowledge would facilitate the development of self-management support strategies using health information sources that are acceptable to older adults. Methods: A cross-sectional design was utilised to investigate where older adults receive their health information and their perceptions of the perceived reliability of these sources. A paper-based survey was completed by 4,066 randomly selected adults (response rate = 46.8%) aged 55 years and older, who were resident in New South Wales, Australia. Results: Doctors (96%), pharmacists (60%) and the Internet (24%) were the most frequent providers of health information. Less than one-fifth of respondents reported having received health information from a nurse (18%). However, the health information sources perceived to be the most reliable were doctors (98%), pharmacists (74%) and nurses (34%). Discussion: Our results suggest that in Australia older adults primarily use doctors as a source of reliable health information and that nurses are under utilized in the provision of health information. The reasons for this need to be further investigated to ensure that nurses play an optimal role in the primary health care team. Although the Internet proved to be a popular source of health information, levels of perceived reliability were comparatively low. Future research should investigate whether the promotion of credible websites by health care professionals can overcome this barrier.
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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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".