Secondary stroke prevention best practice recommendations: exploring barriers for rural family physicians.
Bibliographic record
Abstract
INTRODUCTION: Patients' risk of having a second stroke can be substantially reduced by implementing best practice recommendations for secondary stroke prevention. However, evidence indicates that rural practitioners may face barriers to implementing these recommendations into their practices. This research project developed a workshop to increase practitioner awareness of the recommendations, and to identify barriers to the application of recommendations for secondary prevention of stroke in rural practices. METHODS: The workshop provided a venue for family physicians, specialists and health district representatives to discuss the recommendations. It was evaluated using a sequential explanatory mixed-methods approach using 3 methods of data collection: a questionnaire, documentation of comments made during discussion periods and post-workshop interviews. RESULTS: Participants at the workshop increased their awareness of the recommendations, and they gained an increased appreciation of how they might collaborate with other practitioners and the health district to implement the recommendations. The workshop identified barriers to implementing recommendations, such as miscommunications with the local health district, role conflict among physicians regarding health promotion and difficulties coordinating care with specialists. CONCLUSION: The workshop was an effective venue for improving communication between physicians and the health district and for reducing barriers to the implementation of recommendations.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".