Development of a Pharmacist REferral Program in a primary cARE clinic (PREPARE): A prospective cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Increasing demand for ambulatory health care services has led to the development of primary care multidisciplinary teams that include pharmacists. The objective of this study was to characterize referrals to a pharmacist in a primary care clinic (PCC) based in Chilliwack, British Columbia. METHODS: This prospective cross-sectional study included all patients referred to the PCC pharmacist over 12 months (May 2015 to April 2016). Data regarding the source/reason for referral, patient demographics, medical problems/medications and number/category of identified drug therapy concerns (DTCs) were collected. RESULTS: A total of 137 referrals were received. Mean age was 60 years and 59% were female. Twenty patients (15%) did not attend their appointment. Fifty-eight percent were new clinic patients identified using a Medication Risk Assessment Questionnaire (MRAQ), 30% were from PCC clinicians and 12% were from community family physicians. The most common reason for referral was for a medication review (82%). Median number of medical problems and medications per patient were 7 (interquartile range [IQR] 5) and 11 (IQR 7.5), respectively. A total of 460 DTCs were identified (median 4 per patient, IQR 3.5), of which 34% were medication without an indication and 28% an untreated indication. DISCUSSION AND CONCLUSION: The most common source of referrals to a PCC pharmacist was for medication reviews of new patients using an MRAQ. Most referred patients had multiple medical problems and polypharmacy, and few were referred for disease-specific management. The number of DTCs per patient was variable and, despite polypharmacy being commonplace, almost one-third of patients had an untreated indication.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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 | 0.000 |
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".