TEACH Pilot Study: Implementation of an e-Learning Course in Physical Activity and Sedentary Behaviour for Pre- and In-Service Early Childhood Educators (ECEs)
Bibliographic record
Abstract
Early childhood educators (ECEs) are highly influential in promoting healthy movement behaviours (e.g. physical activity [PA] and sedentary behaviour [SB]) in early childhood. It is essential that ECEs gain knowledge and confidence in their ability to incorporate appropriate amounts of high-quality movement experiences for children in their care. However, ECEs do not currently receive related education in their current pre-service programs or professional development in practice.\nThe Training EArly CHildhood educators in physical activity (TEACH) study intends to improve ECEs’ knowledge, confidence, and intentions regarding promoting healthy movement behaviours by providing comprehensive training in PA, SB, outdoor and risky play in childcare settings via an e-Learning course.\nThe TEACH pilot study tests the implementation (e.g., fidelity, feasibility, acceptability, etc.) of an e-Learning course in PA and SB in a sample of Canadian pre-service (i.e., post-secondary students) and in-service (i.e., practicing) ECEs.\nThe 4-module e-learning course was developed via a Delphi process and was completed by 32 pre-service and 121 in-service ECEs.\nImplementation outcomes were measured cross-sectionally at post-intervention via a process evaluation survey (baseline & follow-up) interviews (transcribed & sorted to implementation outcomes) and e-Learning course metrics (dose delivered, fidelity, complexity & feasibility)\nParticipants reported that the course was highly acceptable, compatible, effective, feasible, and appropriate in complexity; however, some ECEs experienced technical difficulties with the e-Learning platform and noted a longer than anticipated course duration.\nThe findings demonstrate the value of e-Learning for professional development interventions for ECEs. Participant feedback will be used to improve the scalability of this training.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".