Additional file 1 of Evaluating the scaling up of an effective implementation intervention (PACE) to increase the delivery of a mandatory physical activity policy in primary schools
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. Results from sensitivity analysis of linear mixed models illustrating differences in minutes of physical activity scheduled by teachers from baseline compared to 12-month follow-up for only those schools contributing valid data for both baseline and follow-up. *Adjusted for: school factors: type (i.e. government, Catholic, independent), geographical location and socioeconomic disadvantage classification; teacher factors: sex, employment status, years of teaching experience and whether they job share. aDoes not include random slope for time to ensure model fit. bNumber of schools = 69; number of teachers with valid data for unadjusted model = 601; number of teachers with valid data for adjusted model = 534. cNumber of schools = 68; number of teachers with valid data for unadjusted model = 372; number of teachers with valid data for adjusted model = 365. Supplementary Table 2. Results from sensitivity analysis of linear mixed models illustrating differences in minutes of physical activity scheduled by teachers from baseline compared to 12-month follow-up also controlling for region. *Adjusted for: school factors: type (i.e. government, Catholic, independent), geographical location and socioeconomic disadvantage classification; teacher factors: sex, employment status, years of teaching experience and whether they job share and region. aNumber of schools = 88; number of teachers = 591. bNumber of schools = 85; number of teachers = 397
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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 | 0.006 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.839 | 0.068 |
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".