Pharmacist Intervention Program for Control of Hypertension
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical care programs have been shown to improve outcomes in hypertension. However, most programs required direct access to patient medical chart and patient consultation sessions by appointment. OBJECTIVE: To follow the current practice of community pharmacy, exploring the effect of an intervention program on blood pressure (BP) and factors affecting BP. METHODS: Treated hypertensive patients were enrolled in a 9-month controlled study involving 9 community pharmacies. The PRECEDE-PROCEED model was used as conceptual framework to identify factors affecting BP, to incorporate those factors in an intervention program, and to evaluate the impact of the program. A computerized decision-aid tool was used by pharmacists from 4 pharmacies to provide pharmaceutical care to subjects (n = 41); pharmacists from the 5 other pharmacies performed usual care (n = 59). As there was a statistically significant interaction due to family income in describing the impact of pharmacists' intervention on BP, population was stratified by family income in the analyses. RESULTS: Compared with the control group, the pharmacy program resulted in significant systolic BP reduction (-7.8 vs. 0.5 mm Hg; p = 0.01) and an increase in the proportion of controlled patients only for those with high incomes. In the high-income group, the program also had a positive impact on physical activity, self-reported adherence, health concerns, and information transmitted. The low-income group did not appear to benefit from the program. CONCLUSIONS: Pharmacist intervention can modify factors affecting adherence, improve adherence, and reduce BP levels in patients treated with antihypertensive agents. Impact of pharmacist intervention on BP differed according to patient income status.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.008 | 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".