Where low-income seniors receive their heart health and diabetes information: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Older adults (> 55 years), in particular low-income older adults, have lower health literacy than the rest of the Canadian population. Lower health literacy is related to several negative health outcomes such as poor diabetes control and other physical and mental health problems. Canada's rising ageing population requires an age-friendly system that reduces the dependency on the Canadian health care system. This study investigated the Health Information Seeking Behaviour of low-income seniors living in social housing across five Ontario regions to determine how to improve healthcare outcomes and the performance of the Ontario healthcare system. METHODS: This cross-sectional study included in-person interviews guided by the Health Awareness and Behaviour Tool (HABiT) survey. Interviews were conducted with older adults from 16 social housing buildings in five Ontario communities between May 2014 and January 2015. Questionnaire responses were analyzed using descriptive statistics and simple logistics regressions. RESULTS: 625 individuals completed the HABiT survey. The majority of participants sought out health information at the doctor's office; 515 participants received health information from a doctor or nurse about keeping their heart healthy and 471 about preventing diabetes. Females were more than twice as likely to receive health information about heart health from family members, media sources, and pharmacists than males. Those aged > 84 years were the least likely to use media sources and were almost three times as likely to contact a doctor or nurse for heart health information compared to middle-aged participants. Adults with higher post-secondary education were more likely to use the Internet as a source of health information compared to high school graduates. CONCLUSIONS: Family physicians with older adult patients could better supplement their health assessments by promoting and explaining educational brochures, and ensuring that they address these health topics to better communicate chronic disease prevention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".