A qualitative study of evidence in primary care: what the practitioners are saying.
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of evidence-based medicine in primary care. Our objective was to explore the influence of evidence on day-to-day family practice, with specific reference to cardiovascular disease. METHODS: A total of 9 focus groups were conducted in rural, semi-urban and urban settings in Nova Scotia. The participants were 50 family physicians who had practised in their communities for more than 1 year and who were treating patients with cardiovascular disease. FINDINGS: Two major themes emerged: evidence in the clinical encounter and the culture of evidence. The family physicians reported thinking about evidence during the clinical encounter but still situated that evidence within the specific context of their patients and their communities. They appreciated evidence that had been appraised, summarized and published as a guideline by an independent national organization. Evidence remained in the forefront of consciousness for a limited time frame. Local specialists, trusted because of their previous successes with shared patient care, were important sources and interpreters of evidence. INTERPRETATION: Day-to-day family practice offers both obstacles and opportunities for the application of evidence. Although evidence is an important part of clinical practice, it is not absolute and is considered along with many other factors.
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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 | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".