Evaluation of a Worksite-Based Small Group Team Challenge to Increase Physical Activity
Bibliographic record
Abstract
PURPOSE: To investigate whether participants in a small group team challenge had greater completion rates in an institution-wide step-challenge than other participants. DESIGN: A quasi-experimental, posttest-only design with a comparison group was used to evaluate group differences in completion rates. SETTING: A large university system provided the opportunity to participate in a physical activity challenge. PARTICIPANTS: The study was limited to employees who participated in the physical activity challenge. INTERVENTION: Two institutions offered participants the chance to compete as smaller groups of teams within their institution. These team-challenge participants (N = 414) were compared to participants from the same institutions that did not sign up for a team and tracked their steps individually (N = 1454). MEASURES: Participants who reported 50 000 steps per week for 5 of the 6 weeks were classified as challenge completers. We also evaluated total step count and controlled for several potential covariates including age, gender, and body mass index. ANALYSIS: Logistic regression was used to model the dichotomous outcome of challenge completion. RESULTS: Team-challenge participants were more likely to complete the physical activity challenge than other participants. Team-challenge participants had 1922 more steps per day than individual participants. However, at an institution level, overall completion rates were not higher at institutions that offered a team challenge.
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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 | 0.005 | 0.007 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".