Integrating Family Medicine and Pharmacy to Advance Primary Care Therapeutics
Bibliographic record
Abstract
The prevalence of suboptimal prescribing of medications is well documented. Patients are often undertreated or not offered therapeutic treatments that are likely to confer benefit. As a result, drug-related hospital admissions are common and often preventable. Improvements to the health-care system are clearly needed in order to maximize the benefits that can be derived from medications. Many countries are changing their primary health-care systems to improve the quality of health-care delivery. One main transformation is the use of multidisciplinary care teams to provide care in a coordinated manner often from the same location or by using the common medical record of the patients. It has been demonstrated that pharmacists can improve prescribing, reduce health-care utilization and medication costs, and contribute to clinical improvements in many chronic medical conditions, such as cardiovascular disease, diabetes, and psychiatric illness. However, the effect of integrating a pharmacist providing general services into a primary care group has not been extensively studied. The Integrating Family Medicine and Pharmacy to Advance Primary Care Therapeutics (IMPACT) project was designed to provide a real-world demonstration of the feasibility of integrating the pharmacist into primary care office practice. This article provides a description of the IMPACT project participants; the IMPACT practice model and the concepts incorporated in its development; some initial results from the program evaluation; sustainability of the model; and some reflections on the implementation of the practice model.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".