Feasibility of a self-administered survey to identify primary care patients at risk of medication-related problems
Bibliographic record
Abstract
Mark J Makowsky,1 Andrew J Cave,2 Scot H Simpson1 1Faculty of Pharmacy and Pharmaceutical Sciences, 2Department of Family Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada Background and objectives: Pharmacists working in primary care clinics are well positioned to help optimize medication management of community-dwelling patients who are at high risk of experiencing medication-related problems. However, it is often difficult to identify these patients. Our objective was to test the feasibility of a self-administered patient survey, to facilitate identification of patients at high risk of medication-related problems in a family medicine clinic. Methods: We conducted a cross-sectional, paper-based survey at the University of Alberta Hospital Family Medicine Clinic in Edmonton, Alberta, which serves approximately 7,000 patients, with 25,000 consultations per year. Adult patients attending the clinic were invited to complete a ten-item questionnaire, adapted from previously validated surveys, while waiting to be seen by the physician. Outcomes of interest included: time to complete the questionnaire, staff feedback regarding impact on workflow, and the proportion of patients who reported three or more risk factors for medication-related problems. Results: The questionnaire took less than 5 minutes to complete, according to the patient's report on the last page of the questionnaire. The median age (and interquartile range) of respondents was 57 (45–69) years; 59% were women; 47% reported being in very good or excellent health; 43 respondents of 100 had three or more risk factors, and met the definition for being at high risk of a medication-related problem. Conclusions: Distribution of a self-administered questionnaire did not disrupt patients, or the clinic workflow, and identified an important proportion of patients at high risk of medication-related problems. Keywords: screening tool, pharmacists, primary care, medication related problems
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".