How Do Stages of Change for Physical Activity Relate to Employee Sign-Up for and Completion of a Worksite Physical Activity Competition?
Bibliographic record
Abstract
INTRODUCTION: This study evaluated whether stages of change for physical activity (PA) predict sign-up, participation, and completion in a PA competition. METHOD: Deidentified data were provided to evaluate a PA competition between 16 different institutions from a public university system. Employees who completed a health assessment (HA) prior to the start of the PA competition ( n = 6,333) were included in the study. Participants completed a self-report HA and logged their PA throughout the competition. Multivariable logistic regression models tested whether stages of change predicted PA competition sign-up and completion. An ordinal logistic regression model tested whether stages of change predicted number of weeks of PA competition participation. RESULTS: Stages of change predicted PA competition sign-up and completion, but not weeks of participation. The odds for PA competition sign-up were 1.64 and 1.98 times higher for employees in preparation and action/maintenance (respectively) compared with employees in precontemplation/contemplation. The odds for PA competition completion were 4.17 times higher for employees in action/maintenance compared with employees in precontemplation/contemplation/preparation. CONCLUSION: The PA competition was more likely to reach employees in preparation, action, or maintenance stages than precontemplation/contemplation. Most of the completers were likely participating in regular PA prior to the competition.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".