Where Canadian family physicians learn procedural skills.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Little is known about where family physicians learn procedural skills. In this study, we examine where Canadian family medicine graduates learned to do the procedures they perform. METHODS: In 2001, a cross-sectional postal survey was conducted of the 369 family medicine graduates from the University of Alberta and the University of Calgary between 1996 - 2000. From a list of 31 procedures, respondents identified procedures regularly performed over the past 2 years and indicated which procedures they had stopped performing. Respondents indicated whether the procedures performed were learned primarily during medical school and residency, through formal skills training following residency, or in the practice setting. RESULTS: The 282 (76.4% response rate) respondents reported performing a mean of 10.5 (SD=5.3) procedures. The vast majority reported learning procedural skills in medical school or during family medicine residency training (91.1%), followed by the clinical practice setting (12.6%), then formal skills training (6.4%). Those in rural practice learned a relatively greater proportion of procedural skills through formal skills training. CONCLUSIONS: For Canadian family physicians, procedural skill acquisition occurs across the learning continuum. Medical schools and residency training programs play a role in facilitating the learning of procedural skills and supporting self-directed learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".