EXPLORING HEALTHCARE WORKERS' GERIATRIC EDUCATION AND SUBSEQUENT COMMUNICATION WITH OLDER ADULTS
Bibliographic record
Abstract
Abstract Research shows that older adults are living longer than ever, are the fastest growing population, and can have increasingly complex health-related issues. However, the health knowledge and literacy of older adults can be limited, and these adults may have difficulty understanding the terminology that healthcare workers use to communicate with them about their health. For impoverished older adults especially, this can contribute to poor health decisions and decreased care. Given this, educating healthcare practitioners to communicate effectively with older adults becomes essential to older patients’ quality of care. Using predominantly North American studies of healthcare workers’ practices, education, and their interactions with older adults (aged 65-85, primarily), this review paper finds that: i) older adults are responsible for their communication with healthcare workers, but practitioners, because of their implied authority, control the narrative, and therefore it is necessary for them to become more educated in communicating with older adults; ii) some current communication practices by healthcare workers (with older adults) are not reflective of sufficient care; and iii) new gerontology education can foster increased empathy and shared communication practices among healthcare workers, and this can aid patients to better control and have confidence in their healthcare decisions. Social and cultural factors that may explain the health literacy divide in older adults are discussed, as are recommendations and best practices for healthcare workers working with 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".