Examining the confidence and knowledge base of early childhood educators in physical literacy and its application to practice
Bibliographic record
Abstract
Early childhood is a critical period for the development of physical literacy. An increasing number of children attend childcare facilities, where the majority of their day is spent under the care of an early childhood educator (ECE). To date, little is known about the confidence and knowledge base of ECEs in the area of physical literacy and its application to practice. Methods: An online survey was administered to ECEs in British Columbia to examine their confidence and physical literacy knowledge using multiple choice, likert-scale, and open-ended questions. The survey examined ECE knowledge of physical activity, fundamental motor skill patterns, and the capability to detect and correct movement errors. The ECE's were also asked to provide a confidence rating for this knowledge on a scale from 1 (not at all confident) to 10 (highly confident). Results: A total of 217 respondents were recruited and 78 completed the survey (77 F, 1 M). Most participants (82%) were employed currently at Early Childhood Education Centres and 49% of participants self-reported >16 years of experience. General knowledge for physical activity was high; however, the ECEs demonstrated low knowledge for identifying the source of common movement errors. Knowledge was also limited for identifying developmentally appropriate activities to correct the movement error. Overall confidence ratings were consistent across categories (mean = 6.5±1.9). Discussion: Low performance for the detection and correction of common movement errors in early childhood support a need for physical literacy training, despite ECEs reporting moderate confidence in the content area.Acknowledgments: E Jean Burrows is supported by a CIHR Doctoral Research Award
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".