Lifestyle Medicine Education in Health Professionals Curricula: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Non-communicable diseases (NCDs) are the leading cause of mortality and morbidity worldwide. Underlying NCDs are modifiable risk factors, which may be targeted through Lifestyle Medicine (LM). LM is an evidence-based and clinical discipline that supports healthy lifestyle habits. Much of LM integration in practice is rooted in the education afforded within health professional's curricula. The study aimed to determine the effectiveness of LM educational interventions within health professional's curricula on knowledge, competence, self-efficacy/confidence and skills. A systematic review and meta-analysis were conducted with data analyzed using descriptive statistics and a Random Effect Meta-analysis. A total of 14 studies were included. Interventions centered around substance use, nutrition and physical activity with no studies obtained on the sleep health, stress management and social connectedness pillars. Interventions showed a positive impact on improving knowledge standardized mean difference (SMD): 0.71 (95% CI: 0.25-1.18), self-esteem/self-confidence SMD: 1.34 (95% CI: 0.61-2.07), and outcome practice SMD: 0.78 (95% CI 0.29-1.26). There was insufficient power to provide reliable estimates for the attitude outcome. Integrating LM educational interventions within health professional's curricula is promising and recommended to better equip trainees and future health care providers to support patients with the adoption of a healthy lifestyle.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".