Communication and patient participation influencing patient recall of treatment discussions
Bibliographic record
Abstract
CONTEXT: Patient recall of treatment information is a key variable towards chronic disease (CD) management. It is unclear what communication and patient participation characteristics predict recall. OBJECTIVES: To assess what aspects of doctor-patient communication predict patient recall of medication information. To describe lifestyle treatment recall, in CD primary care patients. DESIGN: Observational study within a RCT. SETTING & PARTICIPANTS: Community-based primary care (PC) practices. Family physicians (n=18): practicing >5 years, with a CD patient caseload. Patients (n=159): >40 years old, English speaking, computer literate, off-target hypertension, type II diabetes and/or dyslipidaemia. MAIN VARIABLES: Patient characteristics: age, education, number of CDs. Information characteristics: length of encounter, medication status, medication class. Communication variables: socio-emotional utterances, physician dominance and communication control scores and PACE (ask, check and express) utterances, measured by RIAS. Number of medication themes, dialogue and initiative measured by MEDICODE. MAIN OUTCOME MEASURES: Recall of CD, lifestyle treatment and medication information. RESULTS: Frequency of lifestyle discussions varied by topic. Patients recalled 43% (alcohol), 52% (diet) to 70% (exercise) of discussions. Two and a half of six possible medication themes were broached per medication discussion. Less than one was recalled. Discussing more themes, greater dialogue and patient initiative were significant predictors of improved medication information recall. DISCUSSION: Critical treatment information is infrequently exchanged. Active patient engagement and explicit conversations about medications are associated with improved treatment information recall in off-target CD patients followed in PC. CONCLUSION: Providers cannot take for granted that long-term off-target CD patients recall information. They need to encourage patient participation to improve recall of treatment information.
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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 | 0.006 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".