Improving Health Outcomes for Oklahomans: A Mixed-Methods Evaluation of a Lifestyle Medicine Intervention Program
Bibliographic record
Abstract
Oklahoma has a high rate of chronic disease in comparison to other states in the U.S., currently ranking among the top five worst states for heart disease, chronic lower respiratory disease, cancer, diabetes, and maternal mortality. The purpose of this study was to evaluate the lifestyle medicine (LM) program at a local hospital system in Tulsa, Oklahoma, a health system that serves a patient population with notable health disparities. This mixed-methods evaluation assessed patient data from a representative sample of 63 patients across six distinct cohorts. Quantitative data included pre/post biometric data and pre/post-self-efficacy assessment. SPSS (V29) was used to analyze all quantitative data via descriptive statistics and paired t-tests. Qualitative data were gathered via interviews and focus groups; all interviews were audio-recorded and transcribed verbatim. MAXQDA was used to analyze qualitative data. Results reveal significant weight loss and significant increases in knowledge, self-efficacy, and health behavior change among patients. The findings of this study support the recommendation that Oklahomans should have increased access to LM interventions in hopes of experiencing significant shifts in improved health outcomes and reduced chronic disease risk factors for individuals and at the community level when such interventions are implemented at scale.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".