Canadian national survey of point-of-care ultrasound training in family medicine residency programs.
Bibliographic record
Abstract
OBJECTIVE: To assess the current state of point-of-care ultrasound (POCUS) training in Canadian family medicine residency programs. DESIGN: Cross-sectional survey to evaluate POCUS education in accredited Canadian family medicine residency programs; only 1 completed survey was accepted per residency program. SETTING: Seventeen accredited Canadian family medicine residency programs. PARTICIPANTS: Fourteen directors of family medicine programs across Canada. MAIN OUTCOME MEASURES: Opinions of program directors in family medicine education on the relevance of POCUS in family medicine, and the role of POCUS training in family medicine residency programs. RESULTS: The Web-based, anonymous survey, which was completed during the months of March and April 2016, achieved a response rate of 82% (14 out of 17 program directors). About one-fifth (21%) of program directors reported having an established ultrasound curriculum. Almost all directors (93%) believed that POCUS teaching should be integrated into family medicine residency curricula. Barriers to establishing training included the following: lack of adequate equipment (57%), lack of instructors (57%), lack of available time in the curriculum (57%), and lack of funding available to support training (71%). Seventy-one percent of respondents believed that POCUS could be used in outpatient family medicine clinics to alter clinical decision making. Some potential benefits associated with POCUS in primary care include more rapid diagnosis, improved patient outcomes, and potential to reduce health care costs. CONCLUSION: Although only a few Canadian family medicine residency program directors reported actually having an established ultrasound curriculum, most of them believed that POCUS training should be offered to family medicine residents and that its use could positively affect primary care. A growing number of family medicine residency programs are considering incorporating ultrasound training into their curricula, but resource availability remains a considerable barrier to implementation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".