Naturally occurring workplace facilities to increase the leisure time physical activity of workers: A propensity-score weighted population study
Bibliographic record
Abstract
The benefit of providing access to physical activity facilities at or near work to support the leisure time physical activity (LTPA) of workers is uncertain. We examined the association between access to physical activity facilities at or near work and the LTPA of workers after adjusting for a range of individual and occupational characteristics. Data was obtained from 60,650 respondents to the 2007-2008 Canadian Community Health Survey. Participants were employed adults ≥18 years of age who had no long-term health condition which reduced their participation in physical activity. Latent class analysis determined naturally occurring combinations of physical activity facilities at or near work. Each combination was balanced by 19 individual and occupational covariate characteristics using inverse probability of treatment weights derived from propensity scores. The association between combinations of physical activity facilities at or near work on LTPA level was estimated by multinomial logistic regression. Five different combinations of physical activity facilities were available to respondents at or near work. Data were analyzed in 2017. All possible physical facilities increased the likelihood for LTPA (OR, 2.08, 95% CI, 1.03-4.20) and other combinations were also positively associated. Respondents with no physical activity facilities were characterized as having a low education, low income, high physically demanding work, poor health and mental health, non-white racial background, and being an immigrant. Access to supportive workplace environments can help workers be physically active. Future research should assess a range of personal, social and environmental factors that may be driving this relationship.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".