Our Whole Lives for Hypertension and Cardiac Risk Factors—Combining a Teaching Kitchen Group Visit With a Web-Based Platform: Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Hypertension (HTN) affects millions of Americans. Our Whole Lives: an eHealth toolkit for Hypertension and Cardiac Risk Factors (OWL-H) is an eHealth platform that teaches evidence-based lifestyle strategies, such mindfulness and cooking skills, to improve self-management of HTN. OBJECTIVE: The primary goal of this pilot study was to evaluate the feasibility of OWL-H combined with teaching kitchen medical group visits (TKMGVs) in a low-income population of participants with HTN. METHODS: We conducted a pre-post 8-week study to assess the feasibility of a hybrid program (a web-based 9-module self-management program, which includes mindfulness and Mediterranean and Dietary Approaches to Stop Hypertension diet) accompanied by 3 in-person TKMGVs among patients with HTN. Data including demographics, platform use, and satisfaction after using OWL-H were examined. Outcome data collected at baseline and 8 weeks included the Mediterranean Diet Questionnaire, Hypertension Self-Care Profile Self-Efficacy Instrument, Blood Pressure Knowledge Questionnaire, and the number of self-reported blood pressure readings. For the statistical analysis, we used descriptive statistics, paired sample t tests (1-tailed), and qualitative methods. RESULTS: Of the 25 enrolled participants, 22 (88%) participants completed the study. Participants' average age was 57 (SD 12.1) years, and 46% (11/24) of them reported a household income
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".