Novel Lifestyle Medicine Virtual Referral Clinic: A Quality Improvement Initiative to Increase Value-Based Care
Bibliographic record
Abstract
Lifestyle education is a key component of healthcare, particularly primary care. Multiple studies have shown that dedicated patient education on lifestyle topics, in addition to medication management, creates the most positive outcomes. Improving knowledge on health topics is critical for eliciting positive change in health behaviors. However, education takes time, and access to focused education outside of the office visit can be a challenge. We discuss here a quality improvement initiative to increase value-based care by utilizing telehealth to improve access to Lifestyle Medicine education during the COVID-19 pandemic. We focused on 4 primary objectives including creating a Lifestyle Medicine Virtual Referral Clinic (LMVRC) embedded within our Family Medicine Residency, determining payor mix of referred patients, utilizing a telehealth platform to provide lifestyle medicine education, and finally determining the influence of our services on patient's perceived health. There were many key findings in the development, implementation, and evaluation of the LMVRC that could serve to assist others and our clinic in further development of lifestyle medicine education in the primary care setting. In our experience, a LMVRC embedded in a primary care setting proved to be an effective approach to provide access for patients and advancing value-based care.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".