Diabetes and Cardiovascular Disease Interventions by Community Pharmacists: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review and assess the quality of studies evaluating community pharmacist interventions for preventing or managing diabetes or cardiovascular disease (CVD) and/or their major risk factors. DATA SOURCES: A comprehensive literature search was performed using MEDLINE (1950-February 2011), EMBASE (1980-February 2011), International Pharmaceutical Abstracts (1970-February 2011), Cumulative Index to Nursing and Allied Health Literature (1982-June 2007), and Cochrane Central Register of Controlled Trials (1898-February 2011). Search terms included: community pharmacy(ies), community pharmacist(s), cardiovascular, diabetes, and intervention. The grey literature was searched using the ProQuest Dissertations and Theses, Theses Canada, and OAlster databases. STUDY SELECTION AND DATA EXTRACTION: Articles published in English or French with all study designs were considered for the review. Studies were included if they contained interventions designed to reduce the incidence, risk, or mortality of CVD or diabetes; affect clinical indicators of CVD or diabetes mellitus (including hypertension, dyslipidemia, or hemoglobin A(1c)); and/or improve adherence to treatment strategies. Only studies involving interventions carried out primarily by pharmacists in community pharmacy settings were included. Study quality was assessed using a checklist validated for both randomized and nonrandomized studies. DATA SYNTHESIS: A total of 4142 studies were initially identified, with 40 meeting our inclusion criteria. Eleven studies were randomized controlled trials, 4 were cluster randomized trials, and 2 studies had randomized before-after designs. The remaining studies were controlled before-after (n = 2), cohort (n = 4), and uncontrolled before-after (n = 17) designs. Interventions focused on diabetes (n = 12), hypertension (n = 9), medication adherence (n = 9), lipids (n = 5), evidence-based medication initiation or optimization (n = 3), risk factor prediction scores (n = 1), and body mass index (n = 1). All studies contained interventions focused at the patient level and the majority of studies (34/40) involved interventions directed at both the physician and patient. No specific intervention emerged as superior, and study quality was generally poor, making it difficult to determine the true effect of the interventions. CONCLUSIONS: Poor study quality, time-intensive interventions, and unproven clinical significance warrant the need for further high-quality studies of community pharmacist interventions for preventing or managing diabetes or CVD and/or their major risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".