Patients’ and Caregivers’ Attitudes Toward Deprescribing in Singapore
Bibliographic record
Abstract
BACKGROUND: Knowledge of decision-making preference of patients and caregivers is needed to facilitate deprescribing. This study aimed to assess the perspectives of caregivers and older adults towards deprescribing in an Asian population. Secondary objectives were to identify and compare characteristics associated with these attitudes and beliefs. METHOD: A cross-sectional survey of two groups of participants was conducted using the Revised Patients' Attitudes Towards Deprescribing questionnaire. Descriptive results were reported for participants' characteristics and questionnaire responses from four factors (belief in medication inappropriateness, medication burden, concerns about stopping, and involvement) and two global questions. Correlation between participant characteristics and their responses was analyzed. RESULTS: A total of 1,057 (615 older adults; 442 caregivers) participants were recruited from 10 institutions in Singapore. In which 511 (83.0%) older adults and 385 (87.1%) caregivers reported that they would be willing to stop one or more of their medications if their doctor said it was possible, especially among older adults recruited from acute-care hospitals (85.3%) compared with older adults in community pharmacies (73.6%). Individuals who take more than five medications and those with higher education were correlated with greater agreement in inappropriateness and involvement, respectively. CONCLUSIONS: Clinicians should consider discussing deprescribing with older adults and caregivers in their regular clinical practice, especially when polypharmacy is present. Further research is needed into how to engage older adults and caregivers in shared decision making based on their attitudes toward deprescribing.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".