Deprescribing conversations: a closer look at prescriber–patient communication
Bibliographic record
Abstract
Background: Little is known about the initiation, style and content of patient and healthcare provider communication around deprescribing. We report the findings from a content analysis of audio-recorded discussions of proton pump inhibitor (PPI) and benzodiazepine deprescribing in primary care. Methods: Participants were healthcare providers ( n = 13) from primary care practices ( n = 3) and patients aged ⩾65 ( n = 24) who were chronic users of PPIs or benzodiazepines. The EMPOWER educational brochures were distributed prior to ( n = 15) or after ( n = 9) the patient’s usual healthcare provider appointment. Conversations were audio-recorded and coded using MEDICODE to analyze who initiated different themes, whether they followed a monologue or dialogue style, and to what extent the thematic content addressed issues pertaining to: ‘dosage/instructions,’ ‘medication action and efficacy,’ ‘risk/adverse effects,’ ‘attitudes/emotions,’ ‘adherence’ and ‘follow up.’ Descriptive analysis of the conversations was performed with comparison between patients who received the EMPOWER brochure before or after their appointments. Results: Patients were mostly women (67%) with a mean age of 74 ± 6 years. For PPI users, prior education resulted in a greater proportion of themes initiated by patients (44% versus 17%) and maintaining dialogue-style conversations (48% versus 28%). Among benzodiazepine users, conversation initiation (52% versus 47%) and conversation style was similar between both groups. The content of deprescribing conversations for PPIs revealed that patients and their healthcare providers focused less on ‘dosage/instructions,’ and more on the ‘medication action and efficacy’ and the necessity for ‘follow up.’ Conversations about stopping benzodiazepines were more likely to stagnate on the ‘if’ rather than the ‘how.’ Conclusion: The initiation, style and content of the conversations varied between PPI and benzodiazepine users, suggesting that healthcare providers will need to tailor deprescribing conversations accordingly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".