<i>Activate Your Health</i>: impact of a real-life programme promoting healthy lifestyle habits in Canadian workers
Bibliographic record
Abstract
The workplace has been suggested as a good setting for the promotion of healthy lifestyles. This article examines the impact of Activate Your Health programme, provided over an average of 1.35 years, on employee health and lifestyle habits (actual and intention to improve). Companies selected one of the programme's four options (number of interventions in parentheses): Control (2), Light (8), Moderate (13) and High (14). Employees (n = 524) completed an online questionnaire at baseline and post-intervention. Mixed-effect models and generalized estimating equations models were used, where appropriate. There was an interaction effect of time by option for the number of employees intending to improve sleep habits (p = 0.030): +11.0% in Light (p = 0.013). No significant interaction effect of time by option was observed for body weight, body mass index, number of health problems or lifestyle habits (actual and intention to improve). When stratified by sex, there was an interaction effect of time by option for the number of women intending to improve sleep habits (p = 0.023): -26.1% in Moderate (p = 0.014). There was an interaction effect of time by option for body weight in men (p = 0.001): -0.58 kg in High (p = 0.031) and +2.58 kg in Control (p = 0.005). Other outcomes of interest were stable or improved post-intervention, regardless of option. The Activate Your Health programme allowed employees to maintain or improve outcomes related to health and lifestyle habits. A package like High may be beneficial for body weight regulation in men.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".