Outcomes Across the Value Chain for a Comprehensive Employee Health and Wellness Intervention
Bibliographic record
Abstract
OBJECTIVE: Evaluate a large employer's wellness intervention by studying outcomes across the value chain, and testing Health Engagement's (HE) dose-response relationship to outcomes. METHODS: Evaluation included 37 measures across eight outcomes domains (OD) using repeated measures, analysis of variance and logistic regression. RESULTS: Participants with higher HE had better pre-post percent changes than control: 1.7% higher for Motivation (OD1), 3.4% for Behavior (OD2), 1.0% for Emotion (OD3), 5.8% for Biometrics (OD4), 6.3% for Compliance (OD5), and 5.2% for Claims (OD6). They also had 0.5% less Productivity loss (OD7), and odds of Turnover (OD8) one-quarter to one-half that of control. A dose-response relationship with degrees of HE was also shown. CONCLUSIONS: Three outcomes domains (OD6 to OD8) can be monetized for cost-benefit analysis. Authors recommend, however, staying focused on driving HE and using metrics from all OD to assess value.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".