Medication use in the context of everyday living as understood by seniors
Bibliographic record
Abstract
Recognizing that older adults are among the biggest consumers of medication, and the demographic group most likely to suffer an adverse drug reaction (ADR), this paper details the findings from a recent study on how older adults come to understand medication and its related use. Using a qualitative content analysis method, semi-structured interviews were conducted with 21 individuals from British Columbia, Canada. Study participants ranged in age from 65 to 89 years (male=9, female=11). Using NVIVO(®) 7 software, data were subjected to comparative thematic content analysis in an effort to capture the role of medication use in the context of everyday living as understood by older adults. While there was variability in how older adults come to understand their medication use, an overarching theme was revealed whereby most participants identified their prescription medications as being life-sustaining and prolonging. Deeper thematic content analysis of participant narratives drew attention to three key areas: (A) medications are viewed as a necessary, often unquestioned, aspect of day-to-day life (B) a relationship is perceived to exist between the amount of medications taken and ones current state of health (C) the overall medication experience is positively or negatively influenced by the doctor patient relationship and the assumption that it is the physicians role to communicate medication information that will support everyday living. The article concludes that medical authority and the complexities surrounding medication use need to undergo significant revision if community dwelling older adults are to experience greater success in safely managing their health and medication-related needs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".