Evidence-based cardiovascular care. Family physicians' views of obstacles and opportunities.
Bibliographic record
Abstract
OBJECTIVE To explore obstacles to and opportunities for applying specifi c lifestyle and pharmacologic recommendations on chronic ischemic heart disease.DESIGN Qualitative study.SETTING Rural, town, and city settings in Nova Scotia.PARTICIPANTS Fifty family physicians caring for patients with cardiovascular (CV) disease.METHOD Nine focus groups were conducted, audiotaped, and transcribed.Seven recommendations had been selected for discussion based on their relevance to primary care, strength, and class of supporting evidence.Analysis was guided by grounded-theory methodology.MAIN FINDINGS "Ischemic events" can be powerful motivators for change, whereas the asymptomatic nature of CV risks and distant outcomes can form obstacles.Trust built through previous experiences and the opportunity to repeat important messages can facilitate application of evidence, but patient-physician relationships can also pose obstacles.CONCLUSION Physicians can take steps to improve care, but success at reducing CV risks depends upon active involvement of many health professionals and community resources.Future guideline implementation should focus on patient-oriented issues, such as comorbidity and treatment preferences.
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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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".