Obesity education in the family medicine clerkship: a US and Canadian survey of clerkship directors’ beliefs, barriers, and curriculum content
Bibliographic record
Abstract
BACKGROUND: Despite concerns regarding the increasing obesity epidemic, little is known regarding obesity curricula in medical education. Medical school family medicine clerkships address common primary care topics during clinical training. However, studies have shown that many family physicians feel unprepared at addressing obesity. The purpose of this study was to evaluate factors related to obesity education provided during family medicine clerkships as well as identify future plans regarding obesity education. METHODS: Data were collected through the 2017 Educational Research Alliance (CERA) survey of Family Medicine Clerkship Directors (CDs) in the United States and Canada. Survey items included the level of importance of obesity education, teaching methods, barriers to teaching, and obesity related topics taught during the clerkship. Survey data were summarized and analyzed. RESULTS: The survey response rate was 71.2%. The most frequent barrier to teaching obesity related topics was time constraints (89%). The most commonly taught topics were co-morbid conditions (82.1%), diet (76.9%), and exercise (76.9%). The least commonly taught topics were addressed less than 30% of the time, and included cultural aspects, obesity bias, medications than can cause weight gain, medications to treat obesity, and bariatric surgery. Over half of CDs (59%) are not planning to change existing curriculum, with 39% planning to add to the current curriculum. The CDs' perceptions of the importance of obesity education were significantly associated with the number of topics covered during clerkship (p < 0.001). No relationship was found between clerkship duration and the number of obesity topics taught. CONCLUSION: The majority of clerkship directors are planning no changes to their existing curricula which consist of three common topics: obesity related co-morbid conditions, diet, and exercise. While time was the largest self-rated barrier in teaching obesity related topics, clerkship duration didn't impact the number of topics taught. However, the relative amount of importance placed by CDs upon obesity education was significantly associated with the number of topics covered during clerkship.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".