Family medicine training in housecalls: Survey of residency program directors across Canada.
Bibliographic record
Abstract
OBJECTIVE: To assess the current landscape of home-based primary care (HBPC) or home visit training for Canadian family medicine residents. DESIGN: Online survey. SETTING: Canada's 17 family medicine residency programs. PARTICIPANTS: Family medicine residency program directors. MAIN OUTCOME MEASURES: Program characteristics, current HBPC training, barriers and enablers to training, and program directors' attitudes toward training. RESULTS: There was a 76% response rate (13 of 17 program directors). Respondents' programs ranged in size from 75 to 300 residents (median 160) and closely reflected actual resident distribution of family medicine residents in Canada. Twelve of the 13 programs offered HBPC training including home visit experiences. Six programs had HBPC-related didactic lectures. None of the respondents had a formal program-wide clinical home visit curriculum, and HBPC training availability and requirements varied across programs. The most frequently cited barriers included logistical constraints, limited faculty availability, and safety concerns. Program directors generally agreed that HBPC training is essential to family medicine training, that it provides valuable learning experiences for family medicine residents, and that it effectively prepares residents in core family medicine competencies. None thought that HBPC training was too difficult to coordinate or that its barriers outweighed its educational benefits. CONCLUSION: There is increasing need for HBPC delivery in Canada, and program directors agree that HBPC training is important and worthwhile. However, barriers exist. Current HBPC training in Canada varies in its availability and requirements, and structured program-wide home visit curricula are absent. We recommend development of a central framework for a structured HBPC curriculum that is competency-based and adaptable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".