Attitudes of Older Adults and Caregivers in Australia toward Deprescribing
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Use of harmful and/or unnecessary medications in older adults is common. Understanding older adult and caregiver attitudes toward deprescribing will contribute to medication optimization in practice. The aims of this study were to capture the attitudes and beliefs of older adults and caregivers toward deprescribing and determine what participant characteristics and/or attitudes (if any) predicted reported willingness to have a medication deprescribed. DESIGN: Self-completed questionnaire. SETTING: Australia. PARTICIPANTS: Older adults (n = 386), 65 years or older, taking one or more regular prescription medications and caregivers of older adults (n = 205) who could self-complete a written questionnaire in English. MEASUREMENTS: Older adult and caregiver versions of the validated revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire were completed. The rPATD includes two global questions and four factors: perceived burden of medications, belief in appropriateness of medications, concerns about stopping, and involvement in medication management. Participant characteristics, self-rated health, trust in physician, and health autonomy were also collected. RESULTS: Older adult participants had a median age of 74 years (interquartile range [IQR] = 70-81 y), and caregivers were aged 67 years (IQR = 59-76) and were caring for a person aged 81 years (IQR = 75-86.25 y). Most of both older adults (88%) and caregivers (84%) agreed or strongly agreed that they would be willing to stop one or more of their or their care recipient's medications if their or their care recipient's doctor said it was possible. In a binary logistic regression model, a low concern about stopping factor score was the strongest predictor of willingness to have a medication deprescribed in older adults (odds ratio [OR] = 0.12; 95% confidence interval [CI] = 0.04-0.34). Excellent/good rating of physical health was the strongest predictor in caregivers (OR = 3.71; 95% CI = 1.13-12.23). CONCLUSIONS: Most older adults and caregivers are willing to have one of their or their care recipient's medication deprescribed, although different predictors (characteristics/attitudes) of this willingness were identified in these two groups.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".