Are family medicine residents trained to counsel patients on physical activity? The Canadian experience and a call to action
Bibliographic record
Abstract
Physical inactivity is a leading risk factor for non-communicable diseases (NCDs) and early mortality. Family physicians have an important role in providing physical activity counselling to patients to help prevent and treat NCDs. Lack of training on physical activity counselling is a barrier in undergraduate medical education, yet little is known regarding physical activity teaching in postgraduate family medicine residency. We assessed the provision, content and future direction of physical activity teaching in Canadian postgraduate family medicine residency programs to address this data gap. Fewer than half of Canadian Family Medicine Residency Programme directors reported providing structured physical activity counselling education to residents. Most directors reported no imminent plans to change the content or amount of teaching. These results reflect significant gaps between the recommendations of WHO, which calls on doctors to prescribe physical activity, and the current curricular content and needs of family medicine residents. Almost all directors agreed that online educational resources developed to assist residents in physical activity prescription would be beneficial. By describing the provision, content and future direction of physical activity training in family medicine, physicians and medical educators can develop competencies and resources to meet this need. When we equip our future physicians with the necessary tools, we can improve patient outcomes and do our part to reduce the global epidemic of physical inactivity and chronic disease.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".