Medication Adherence and Beyond: Blood Pressure Control and the Pharmacist
Bibliographic record
Abstract
2011 marked the 12th year in which the Canadian Hypertension Education Program (CHEP) released annual guidelines for the diagnosis and treatment of hypertension, and the 6th year that a targeted pharmacist version of these guidelines has been developed.1 The latest versions of the guidelines emphasize the important role of adherence to both medication and lifestyle strategies. The pharmacist-specific recommendation in the CHEP guidelines indicates: “Health care professional interventions can reduce nonadherence and improve adherence in those who are having problems. Integrating pharmacists into the care of people with hypertension improves blood pressure control.”2 Assessing adherence and intervening when poor adherence is noted are important elements of pharmaceutical care. As pharmacists, we understand the complexity of adherence and the multiple factors that play a role in helping patients initiate and/or maintain a behaviour change, including lifestyle or use of medications. Communication is necessary to identify and understand whether our patients will follow through with the recommended behavioural change, and the relationship that exists between the pharmacist and the patient may play a role in honest communication, thus affecting adherence either positively or negatively. Since adherence to antihypertensives has been shown to decrease hospitalization rates, offsetting medication costs for this chronic condition,3 it is a vital area for pharmacist involvement.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".