A scoping review of obesity education interventions for current and prospective medical professionals in Canada
Bibliographic record
Abstract
Background: Obesity is a prevalent chronic disease in Canada. Individuals living with obesity frequently interact with medical professionals who must be prepared to provide evidence-based and person-centred care options. The purpose of this scoping review was to summarize existing educational interventions on obesity in Canada for current and prospective medical professionals and to identify key future directions for practice and research. Methods: A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. The search strategy was conducted using Medline (via PubMed), Embase, Eric, CBCA, Proquest Education, and Proquest Theses. The inclusion criteria included delivery of an educational intervention on obesity for current medical professionals, medical undergraduate trainees, or residents administered in Canada. Data were extracted from the included studies to thematically summarize the intervention content, and main outcomes assessed. Future directions for research and practice were identified. Results: Eight studies met the inclusion criteria. The interventions ranged in terms of the mode of delivery, including interactive in-person workshops and seminars, online learning modules, webinars, and videos. The main outcomes assessed were attitudes towards patients living with obesity, self-efficacy for having sensitive obesity-related discussions, skills to assess obesity and provision of management options. All studies reported improvements in the outcomes. Future directions identified were the need to develop standardized obesity competencies for inclusion across medical education programs, further research on effective pedagogical approaches to integrating content into existing curricula and the need for broader awareness and assessment of the quality of obesity education resources. Conclusion: Although there have been few obesity-specific educational interventions for current and prospective medical professionals in Canada, existing evidence shows positive learning outcomes. These findings advocate for continued investment in the development of obesity medical training and educational interventions.
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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 | 0.031 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.024 | 0.037 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".