Implementation of an e-Learning course in physical activity and sedentary behavior for pre- and in-service early childhood educators: Evaluation of the TEACH pilot study
Bibliographic record
Abstract
Abstract Background Childcare-based physical activity (PA) and sedentary behavior (SB) interventions have traditionally used in-person training to supplement early childhood educators’ (ECEs) knowledge and confidence to facilitate physically active programming for the children in their care. However, this method of delivery is resource-intensive and unable to reach a high number of ECEs. The purpose of the Training pre-service EArly CHildhood educators in PA (TEACH) pilot study was to test the implementation (e.g., fidelity, feasibility, acceptability) of an e-Learning course targeting PA and SB among a sample of pre-service (i.e., post-secondary students) and in-service (i.e., practicing) ECEs in Canada. Methods A pre-/post-study design was adopted for this pilot study, and implementation outcomes were assessed cross-sectionally at post-intervention. Pre-service ECEs were purposefully recruited from three Canadian colleges and in-service ECEs were recruited via social media. Upon completing the e-Learning course, process evaluation surveys (n = 32 pre-service and 121 in-service ECEs) and interviews (n = 3 pre-service and 8 in-service ECEs) were completed to gather ECEs’ perspectives on the e-Learning course. Fidelity was measured via e-Learning course metrics retrieved from the web platform. Descriptive statistics were calculated for quantitative data, and thematic analysis was conducted to analyze qualitative data. Results Moderate-to-high fidelity to the TEACH study e-Learning course was exhibited by pre-service (68%) and in-service (63%) ECEs. Participants 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. The most enjoyed content for pre- and in-service ECEs focused on outdoor play (87.5% and 91.7%, respectively) and risky play (84.4% and 88.4%, respectively). Conclusions These findings demonstrate the value of e-Learning for professional development interventions for ECEs. Participant feedback will be used to make improvements to the TEACH e-Learning course to improve 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 | 0.017 | 0.020 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".