When Does Pharmaceutical Care Impact Health Outcomes? A Comparison of Community Pharmacy—Based Studies of Pharmaceutical Care for Patients with Asthma
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical care (PC) as a philosophy of care and practice model is now >14 years old. It is important to determine whether PC influences health outcomes. Such outcomes are best studied in specific disease states where variables are minimized and specific outcomes have been established. We analyzed 4 multi-site controlled studies that evaluated PC in community pharmacies for patients with asthma. Study results varied widely. OBJECTIVE: To understand factors contributing to positive outcomes from PC for asthma. METHODS: The 4 studies were compared on the basis of 10 aspects of their research design, as well as 10 elements of PC. Dr. McLean conducted the initial analysis, and his assessments were confirmed by Dr. MacKeigan. RESULTS: Important differences were found in the type of pharmacy where PC was delivered (chain vs independent), how pharmacies were selected (required vs volunteered), patient selection (on asthma medication vs uncontrolled disease), pharmacist training (4-h workshop vs certification over several weeks), the nature of PC protocol (computer reminders vs detailed care protocol), rigor of the protocol (intervention vs requirement to reach self-management), and the level of pharmacist adherence to the PC protocol (<50% vs 90%). Differences were also found in study design. CONCLUSIONS: More favorable PC outcomes were associated with use of all elements of PC, independent pharmacies, pharmacist certification, a detailed PC protocol, targeting patients with uncontrolled asthma, and a practice system facilitating PC.
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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 | 0.061 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".