Transforming Health and Resiliency Through Integration of Values-based Experiences: Implementation of an Electronic Evidence-based Whole Health Clinical Program
Bibliographic record
Abstract
BACKGROUND: Complementary and integrative health (CIH) is the foundation of the Department of Veterans Affairs (VA) Whole Health System program (WH), including Transforming Health and Resiliency through Integration of Values-based Experiences (THRIVE). The global COVID-19 pandemic prompted an urgent need to provide services such as THRIVE while following guidelines for social distancing. OBJECTIVE: The objective of this paper was to describe the systematic implementation of THRIVE using an electronic delivery model. METHODS: The study involved an observational clinical program implementation project using the RE-AIM framework to contextualize the implementation strategies and results, and then the implementation of an electronically delivered CIH group medical appointment program (eTHRIVE). RESULTS: Clinical staff transitioned to 100% electronic delivery of the THRIVE curriculum using the new eTHRIVE delivery model. The current electronic delivery model, eTHRIVE, has effectively enrolled 10-12 veterans per cohort, with 8 cohorts, totaling 87 veterans to date. eTHRIVE attrition has been 6% (5/87) since initiation. CONCLUSIONS: The current climate of the VA WH programmatic initiative combined with the public health needs during a global pandemic prompted the move of THRIVE program into an electronic format to broaden scalability and reach.
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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 | 0.088 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".