A Web-Based Lifestyle Medicine Curriculum: Facilitating Education About Lifestyle Medicine, Behavioral Change, and Health Care Outcomes
Bibliographic record
Abstract
BACKGROUND: Lifestyle medicine is the science and application of healthy lifestyles as interventions for the prevention and treatment of disease, and has gained significant momentum as a specialty in recent years. College is a critical time for maintenance and acquisition of healthy habits. Longer-term, more intensive web-based and in-person lifestyle medicine interventions can have a positive effect. Students who are exposed to components of lifestyle medicine in their education have improvements in their health behaviors. A semester-long undergraduate course focused on lifestyle medicine can be a useful intervention to help adopt and sustain healthy habits. OBJECTIVE: To describe a novel, evidence based curriculum for a course teaching the concepts of Lifestyle Medicine based on a web-based course offered at the Harvard Extension School. METHODS: The course was delivered in a web-based format. The Lifestyle Medicine course used evidence based principles to guide students toward a "coach approach" to behavior change, increasing their self-efficacy regarding various lifestyle-related preventive behaviors. Students are made to understand the cultural trends and national guidelines that have shaped lifestyle medicine recommendations relating to behaviors. They are encouraged to engage in behavior change. Course topics include physical activity, nutrition, addiction, sleep, stress, and lifestyle coaching and counseling. The course addressed all of the American College of Preventive Medicine/American College of Lifestyle Medicine competencies save for the competency of office systems and technologies to support lifestyle medicine counseling. RESULTS: The course was well-received, earning a ranking of 4.9/5 at the school. CONCLUSIONS: A novel, semester-long course on Lifestyle Medicine at the Harvard Extension School is described. Student evaluations suggest the course was well-received. Further research is needed to evaluate whether such a course empowers students to adopt behavior changes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".