“At 80 I Know Myself”: Embodied Learning and Older Adults’ Experiences of Polypharmacy and Perceptions of Deprescribing
Bibliographic record
Abstract
In response to the risks of polypharmacy for older adults, there are increasing calls for the development and implementation of deprescribing programs. This article examines the forms of expertise that inform older adults' decisions about how to use medications given concerns over polypharmacy and a clinical focus on deprescribing. In-depth interviews with older adults found that diverse knowledge sources underpin decisions regarding polypharmacy and deprescribing. Findings indicate that this knowledge is formed through a lifetime of embodied learning-the production of relevant knowledge through lived experiences of the body. By way of this embodied learning, older adults possess individualized knowledge bases that inform health and health care decisions, especially regarding the use of medications. If deprescribing programs are to be embedded into standard preventive medical care of older adults, then it is valuable for health care providers to be aware of and take seriously the contribution of embodied knowledge.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".