Attitudes and beliefs of older adults and caregivers towards deprescribing in French-speaking countries: a multicenter cross-sectional study
Bibliographic record
Abstract
Abstract Purpose Successful deprescribing requires understanding the attitudes of older adults and caregivers towards this process. This study aimed to capture these attitudes in four French-speaking countries, and to investigate associated factors. Methods A multicenter cross-sectional study was conducted by administrating the French version of the revised Patients’ Attitudes Towards Deprescribing (rPATD) questionnaire in Belgium, Canada, France and Switzerland. Community-dwelling or nursing home older adults ≥ 65 years taking ≥ 1 prescribed medications, and caregivers of older adults with similar characteristics were included. Multivariate logistic regressions were carried out to examine factors associated with willingness to deprescribe. Results A total of 367 older adults (79.3 ± 8.7 years, 63% community-dwelling, 54% ≥5 medications) and 255 unrelated caregivers (64.4 ± 12.6 years) of care recipients (83.4 ± 7.9 years, 52% community-dwelling, 69% ≥5 medications) answered the questionnaire. Among them, 87.5% older adults and 75.6% caregivers would be willing to stop medications if the physician said it was possible. Reluctance to stop a medication taken for a long time was expressed by 46% of both older adults and caregivers. A low score for the factor “concerns about stopping” [older adults: aOR: 0.21; 95%CI: 0.07–0.59], and a high score for the factor “involvement” [older adults: aOR: 2.66; 95%CI: 1.01–7.07; caregivers: aOR: 11.28; 95%CI: 1.48–85.91] were associated with willingness to deprescribe. Conclusions A significant proportion of older adults and caregivers of French speaking countries are open to deprescribing. Despite this apparent willingness, deprescribing conversations in clinical practice remains marginal, emphasizing the importance of optimizing the integration of existing tools such as rPATD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".