Relationship between number, timing, and type of pharmacist interventions and patient outcomes
Bibliographic record
Abstract
Pharmacy practice-based research has demonstrated that enhanced pharmacist care improves patient care and outcomes. Such programs have encompassed a wide variety of disease states, including hypertension, asthma, smoking, cardiovascular problems, and high cholesterol.1,–7 Among the key features of enhanced care programs are multifaceted interventions whereby pharmacists make treatment recommendations to physicians, provide patient education, monitor patient response to therapy, and enhance compliance with medications and nonpharmacologic management strategies. Since practice-based research interventions are usually multifaceted, it is important to know which components are most important in achieving the desired outcomes (establishing the “mechanism of action”), and how differing levels of application of the intervention affect the outcome (“dose–response relationship”). Invariably, the intensity (dose) of the intervention will vary with different practice settings, the experience and motivation of the investigator, and other factors.8,9 SCRIP-plus (the Second Study of Cardiovascular Risk Intervention by Pharmacists) was a multicenter, prospective, before–after study of a community pharmacy intervention in patients at very high risk for cardiovascular events.10 The SCRIP-plus study found a mean ± S.D. decrease in low-density-lipoprotein cholesterol (LDL-C), the primary endpoint, of 0.5 ± 0.83 mmol/L (19.4 ± 32.1 mg/dL). The large standard deviation suggests variability in the efficacy of the pharmacist-influenced covariates or mechanisms involved in achieving this result. Examination of the relationship between the application of the intervention and the outcome has important implications for the design of future pharmacy practice-based research studies, specifically the design of the intervention itself.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".