Obesity management in primary care: Are we adequately preparing the next generation of Canadian family physicians?
Bibliographic record
Abstract
Background: Obesity is a chronic disease that affects a large proportion of the population. We examined the preparation of Canadian medical learners for obesity management through three cross-sectional studies exploring the: 1) knowledge of evidence-based obesity management among medical students; 2) perspectives of family medicine residents on the adequacy of obesity management training in their residency programs; and 3) intentions of family medicine residents regarding obesity management when they enter practice. Methods: An online survey instrument was developed for each study. For Study 1, the survey was developed by HP, VE and JA; the questions mapped onto the ten domains outlined in the 2020 Canadian Clinical Practice Guidelines on the Management of Obesity in Adults. For Study 2, the survey was developed by HP and TMcC; the questions aligned with competencies in the management of other chronic diseases. For Study 3, the survey was developed by MR, HP and MV; the Capability, Opportunity, and Motivation (COM-B) model was used as a framework for the questions. All three surveys met the criterion for face validity and the survey for Study 3 met criteria for content and criterion validity. Results: Study 1 enrolled 26 first-year and 22 fourth-year students. Total correct scores were below 50 % for both groups, and patient-centeredness scores differed significantly between the groups. Study 2 enrolled 494 family medicine residents; less than 25 % thought there was sufficient time devoted to training in obesity management during residency and approximately 75 % experienced inconsistent messaging about obesity management from their preceptors. Study 3 enrolled 150 family medicine residents. Few participants believed that they had been well trained or had a clear plan for managing obesity, but most believed that obesity management will form part of their practice and that they will have the time and resources for obesity management. Conclusion: Current medical education does not reflect the requisite knowledge for contemporary obesity management. Further, family medicine residents identified gaps in mentorship of contemporary principles of obesity management. These findings support the revision of the medical curriculum in Canada to better reflect the science of obesity and its management.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".