Change in pre- and in-service early childhood educators’ knowledge, self-efficacy, and intentions following an e-learning course in physical activity and sedentary behaviour: a pilot study
Bibliographic record
Abstract
BACKGROUND: Early childhood educators (ECEs) are the primary daytime role models for many young children, and are responsible for facilitating physical activity (PA) opportunities and minimizing sedentary behaviour (SB) in childcare. However, they have reportedly received little related education in their pre-service training. The purpose of the Training pre-service EArly CHildhood educators in physical activity (TEACH) pilot study was to explore changes in pre- and in-service ECEs' knowledge, self-efficacy, behavioural intention, and perceived behavioural control following the TEACH e-Learning course in PA and SB. METHODS: Pre-service ECEs were purposefully recruited from three Canadian colleges, while in-service ECEs were recruited via social media. A pre-post study design was used. ECEs completed two online surveys; one prior to, and one immediately following the completion of the TEACH e-Learning course (~ 5 h). Descriptive statistics were reported, and McNemar Chi-Square tests and paired samples t-tests were used to examine changes in ECEs' question-specific, and total knowledge scores, respectively. Wilcoxon Signed Ranks tests were employed to examine changes in self-efficacy, behavioural intention, and perceived behavioural control. RESULTS: Both pre- (n = 32) and in-service (n = 121) ECEs significantly increased their total knowledge scores from pre- to post-course completion (p < .05*). Significant positive changes in self-efficacy (p < .025*), behavioural intention (p < .007*), and perceived behavioural control (p < .007*) were demonstrated by in-service ECEs following course completion, while only select composite scores within these tools were significant among pre-service ECEs. CONCLUSIONS: These findings provide preliminary evidence of the potential efficacy of the e-Learning course at improving ECEs' knowledge, self-efficacy, behavioural intention, and perceived behavioural control to support PA and minimize SB in childcare. Following the success of the pilot study, testing the effectiveness of the TEACH e-Learning course on a larger scale, with a comparison group, is warranted prior to recommending broader dissemination of the training in pre-service ECE programs and for in-service ECE professional learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".