Older Adults’ Awareness of Deprescribing: A Population‐Based Survey
Bibliographic record
Abstract
OBJECTIVES: To determine older adults' awareness of the concept of medication-induced harm and their familiarity with the term "deprescribing." Secondary objectives were to ascertain determinants of self-initiated deprescribing conversations and to identify how older adults seek information on medication harms. DESIGN: Cross-sectional population-based household telephone survey using random-digit dialling. SETTING: Canada. PARTICIPANTS: Community-dwelling adults aged 65 and older (N = 2,665; n = 898 men, n = 1,767 women, mean age 74.9 ± 7.2, range 65-100). MEASUREMENTS: Information was gathered on age; sex; awareness of the term "deprescribing"; knowledge and information-seeking behaviors related to medication harms; and previous initiation of a deprescribing conversation with a healthcare professional. Three targeted classes of potentially inappropriate prescriptions were asked about: sedative-hypnotics, glyburide, and proton pump inhibitors. Descriptive statistics and regression analyses were used to quantify associations. RESULTS: Two-thirds (65.2%, 95% confidence interval (CI) = 63.4-67.0%) of participants were familiar with the concept of medication-induced harms. Only 6.9% (95% CI = 5.9-7.8%) recognized the term deprescribing; 48% (95% CI = 46-50%) had researched medication-related harms. Older adults most commonly sought information from the Internet (35.5%, 95% CI = 33.4-37.6%), and from health care professionals (32.2%, 95% CI = 30.1-34.3%). Patient-initiated deprescribing conversations were associated with awareness of medication harms (odds ratio (OR) = 1.74, 95% CI = 1.46-2.07), familiarity with the term deprescribing (OR = 1.55, 95% CI = 1.13-2.12), and information-seeking behaviors (OR = 4.57, 95% CI = 3.84-5.45), independent of age and sex. CONCLUSION: Healthcare providers can facilitate patient-initiated deprescribing conversations by providing information on medication harms and using the term "deprescribing."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".