Training Pre-Service Early Childhood Educators in Physical Activity (TEACH): Protocol for a Quasi-Experimental Study
Bibliographic record
Abstract
Background: Early childhood educators (ECEs) influence young children’s early uptake of positive health behaviours in childcare settings and serve as important daytime role models. As such, it is imperative that post-secondary early childhood education programs provide students with the foundational knowledge and professional training required to confidently facilitate quality active play opportunities for young children. The primary objective of the Training pre-service EArly CHildhood educators in physical activity (TEACH) study is to develop and implement an e-Learning course in physical activity and sedentary behaviour to facilitate improvements in: pre-service ECEs’ self-efficacy and knowledge to lead physical activity and outdoor play opportunities and minimize sedentary behaviours in childcare. This study will also explore pre-service ECEs’ behavioural intention and perceived control to promote physical activity and outdoor play, and minimize sedentary behaviour in childcare, and the implementation of the e-Learning course. Methods/Design: A mixed-methods quasi-experimental design with three data collection time points (baseline, post-course completion, 3-month follow-up) will be employed to test the e-Learning course in early childhood education programs (n = 18; 9 experimental, 9 comparison) across Canada. Pre-service ECEs enrolled in colleges/universities assigned to the experimental group will be required to complete a 4-module e-Learning course, while programs in the comparison group will maintain their typical curriculum. Pre-service ECEs’ self-efficacy, knowledge, as well as behavioural intention and perceived behavioural control will be assessed via online surveys and module completion rates will be documented using website metrics. Group differences across timepoints will be assessed using linear mixed effects modelling and common themes will be identified through thematic analysis. Discussion: The TEACH study represents a novel, evidence-informed approach to address the existing gap in physical activity and sedentary behaviour-related education in Canadian post-secondary early childhood education programs. Moreover, e-Learning platforms, can be employed as an innovative, standardized, and scalable way to provide ECEs with consistent training across jurisdictions.
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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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.021 |
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".