Undergraduate medical education interventions aimed at managing patients with obesity: A systematic review of educational effectiveness
Bibliographic record
Abstract
The growing obesity epidemic requires an evidence-based approach to management of patients with obesity. Two systematic reviews on obesity-management interventions in undergraduate medical education, both published in 2012, reported discrepant findings. This study aimed to build on previous research by identifying, systematically reviewing, and synthesizing current literature on the effectiveness of educational interventions aimed at teaching management of patients with obesity to medical students. A comprehensive search of seven databases was performed with no date or language restrictions. Database search identified 6462 studies; 5373 were screened against title and abstract, 156 full-text articles were retrieved, 31 met eligibility criteria, and 17 were included after critical appraisal of study methodology. Nine cohort-studies, three qualitative, two case-controls, two mixed-methods, and one randomized controlled trial were included. Findings supported the educational effectiveness of brief (<3 h) educational interventions, the value of video-clips to deliver content, and the importance of in-person teaching. Findings also demonstrated an increase in the number of studies describing educational interventions aimed at teaching management of patients with obesity to medical students. These results can be used by medical educators to inform the design of educationally effective curricula focused on the management of patients with obesity in undergraduate medical education.
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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".