Strategies for enhancing the initiation of cholesterol lowering medication among patients at high cardiovascular disease risk: a qualitative descriptive exploration of patient and general practitioners’ perspectives on a facilitated relay intervention in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: The objective of our study was to explore the perspectives of patients and general practitioners (GPs) regarding interventions to increase initiation of cholesterol lowering medication (or statins), including a proposed laboratory-based facilitated relay intervention. DESIGN: Qualitative descriptive study using interviews and focus groups for data collection, and thematic analysis for data analysis. SETTING: Primary care providers and patients in Calgary, Alberta, Canada. PARTICIPANTS: 17 GPs with primarily community-based, non-academic practices with at least 1 year of practice experience participated in semistructured interviews. 14 patients at high risk of cardiovascular disease participated in focus groups. MAIN OUTCOME MEASURES: Exploration of strategies that might be used to enhance the prescription of, and adherence to statin therapy for patients with statin-indicated conditions. RESULTS: GPs proposed a variety of interventions to improve statin prescription, including electronic record audit solutions, GP directed education, and patient-oriented campaigns. Patients expressed that they may benefit from being provided access to their laboratory test results, as well as targeted education. Both parties provided positive feedback on the proposed laboratory-based facilitated relay intervention, while pointing out areas for improvement. Notably, GPs were concerned that the patient-directed component of the intervention might jeopardise therapeutic relationships, and patients were concerned about accidental disclosure of personal health information. Important considerations for the design of facilitated relay messaging should include brevity, simplicity and the provision of contact information for inquiries. CONCLUSIONS: GPs and patients described several suggestions for increasing statin initiation and welcomed the proposal of a laboratory-based facilitated relay strategy. These findings support further testing of this intervention which may enhance GPs' ability to successfully engage patients in cardiovascular risk reduction through statin therapy.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".