Consumer Attitudes Towards Deprescribing: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Harmful and/or unnecessary medication use in older adults is common. This indicates deprescribing (supervised withdrawal of inappropriate medicines) is not happening as often as it should. This study aimed to synthesize the results of the Patients' Attitudes Towards Deprescribing (PATD) questionnaire (and revised versions). METHODS: Databases were searched from January 2013 to March 2020. Google Scholar was used for citation searching of the development and validation manuscripts to identify original research using the validated PATD, revised PATD (older adult and caregiver versions), and the version for people with cognitive impairment (rPATDcog). Two authors extracted data independently. A meta-analysis of proportions (random-effects model) was conducted with subgroup meta-analyses for setting and population. The primary outcome was the question: "If my doctor said it was possible, I would be willing to stop one or more of my medicines." Secondary outcomes were associations between participant characteristics and primary outcome and other (r)PATD results. RESULTS: We included 46 articles describing 40 studies (n = 10,816 participants). The meta-analysis found the proportion of participants who agreed or strongly agreed with this statement was 84% (95% CI 81%-88%) and 80% (95% CI 74%-86%) in patients and caregivers, respectively, with significant heterogeneity (I2 = 95% and 77%). CONCLUSION: Consumers reported willingness to have a medication deprescribed although results should be interpreted with caution due to heterogeneity. The findings from this study moves toward understanding attitudes toward deprescribing, which could increase the discussion and uptake of deprescribing recommendations in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".