Association of Teacher‐Level Factors With Implementation of Classroom‐Based Physical Activity Breaks
Bibliographic record
Abstract
BACKGROUND: Classroom-based physical activity (CBPA) breaks are a common strategy to increase elementary school children's physical activity (PA) levels. There is limited research examining how teacher-level factors impact teacher implementation of CBPA breaks. In this study, we assessed the relationship of teacher-level factors with teacher use of a CBPA resource. METHODS: We randomized 6 elementary schools in rural Oregon into control (N = 3) or intervention (N = 3) conditions. Each teacher at intervention schools received the CBPA resource. Teachers at control schools received 1 CBPA-Toolkit per grade level to share, and received no training. We surveyed teachers on their use of the toolkit, implementation support and self-efficacy, and value for PA. Logistic regression was used to examine the odds of toolkit use by teacher-level factors. RESULTS: Among survey respondents (N = 83), 57% were self-identified toolkit users and 48% attended a training. Training participation and teacher implementation self-efficacy were associated with greater odds of using the toolkit (odds ratio, OR = 7.76 [95% confidence interval, CI = 1.39-43.19] and OR = 5.54 [95% CI = 1.24-23.87], respectively). CONCLUSION: CBPA tools supported with training aimed at developing teachers' implementation self-efficacy increased the likelihood of teachers employing CBPA tools.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".