Medical students' perceived comfort and competence performing physical examinations on patients with obesity: A mixed‐methods needs assessment
Bibliographic record
Abstract
Physicians are unsatisfied with their training in the care of patients with obesity. Physical examination is a key component of care, and modifications to techniques are often necessary for patients with obesity. To determine learning needs, we examined medical students' perceived comfort and competency in conducting physical examinations on patients with obesity. This mixed-methods study of Canadian medical students used a questionnaire and semi-structured focus groups to assess medical students' perceived comfort and competence in examining patients with obesity. Participants included 175 Canadian medical students. A minority of medical students felt comfortable (42%) or competent (14%) examining patients with obesity. Physical exam challenges included modifying exam manoeuvres, interpreting findings and communicating sensitively around weight. Lack of early exposure to patients with obesity, minimal instruction by preceptors and a lack of curricular focus on obesity were felt to be barriers to improving these skills. Students perceived their lack of confidence as negatively impacting their ability to manage patients with obesity and more training in this area was desired to prevent disparities in care. Medical students feel that adequate training on how to perform an obesity-specific physical examination is lacking. Developing curricula and including formal teaching around these key competencies within medical education is essential.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".