Implementation of a Self‐Administered Questionnaire to Identify Patients at Risk for Medication‐Related Problems in a Family Health Center
Bibliographic record
Abstract
STUDY OBJECTIVE: To determine if a self-administered questionnaire can improve the identification of patients at risk for medication-related problems (MRPs) compared with usual methods of referral to a pharmacist. DESIGN: Prospective, randomized controlled study. SETTING: Multiprofessional primary care clinic at a tertiary care teaching hospital. PATIENTS: One hundred ninety-four ambulatory patients aged 18 years or older who were taking at least two drugs. MEASUREMENTS AND MAIN RESULTS: Patients completed a five-item, self-administered questionnaire modified from a tool that was previously validated in another population and statistically correlated with the risk of MRPs. Of 194 patients who completed the questionnaire, 89 were randomized to the control group (referral by usual methods) and 105 were referred according to their responses on the questionnaire (intervention group). Primary outcomes were the rate of referral and the number of at-risk patients identified. Referral rates were higher with the questionnaire than with usual methods (20% vs 6%, p=0.003). Of five patients referred by usual methods, one was at risk for MRPs according to questionnaire results. Of 84 patients in the control group who were not referred, 12 (14%) were at risk according to the questionnaire results; this finding suggested that several at-risk patients who were not referred by usual methods might have benefitted from a referral for a pharmacist's assessment. CONCLUSION: This self-administered medication risk assessment questionnaire effectively complemented the usual practices for identifying and referring patients at risk for MRPs.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".