Does a Consumer-Targeted Deprescribing Intervention Compromise Patient-Healthcare Provider Trust?
Bibliographic record
Abstract
One in four community-dwelling older adults is prescribed an inappropriate medication. Educational interventions aimed at patients to reduce inappropriate medications may cause patients to question their prescriber’s judgment. The objective of this study was to determine whether a patient-focused deprescribing intervention compromised trust between older adults and their healthcare providers. An educational brochure was distributed to community-dwelling older adults by community pharmacists in order to trigger deprescribing conversations. At baseline and 6-months post-intervention, participants completed the Primary Care Assessment Survey, which measures patient trust in doctors and pharmacists. Changes in trust were ascertained post-intervention. Proportions with 95% confidence intervals (CI), and logistic regression were used to determine a shift in trust and associated predictors. 352 participants responded to the questionnaire at both time points. The majority of participants had no change or gained trust in their doctors for items related to the choice of medical care (78.5%, 95% CI = 74.2–82.8), communication transparency (75.4%, 95% CI = 70.7–79.8), and overall trust (81.9%, 95% CI = 77.9–86.0). Similar results were obtained for participants’ perceptions of their pharmacists, with trust remaining intact for items related to the choice of medical care (79.4%, 95% CI = 75.3–83.9), transparency in communicating (82.0%, 95% CI = 78.0–86.1), and overall trust (81.6%, 95% CI = 77.5–85.7). Neither age, sex nor the medication class targeted for deprescribing was associated with a loss of trust. Overall, the results indicate that patient-focused deprescribing interventions do not shift patients’ trust in their healthcare providers in a negative direction.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".