Measuring Early Childhood Educators’ Physical Activity and Sedentary Behavior–Related Self-Efficacy: A Systematic Review of Tools
Bibliographic record
Abstract
Early childhood educators’ (ECEs) self-efficacy is often predictive of their ability and likelihood of promoting healthy activity behaviors in childcare settings. To date, ECEs’ physical activity and sedentary behavior–related self-efficacy has been measured in a variety of ways in childcare-based research, creating difficulty when comparing across studies. To identify the different approaches ECEs’ self-efficacy is assessed, the current study aimed to compare all existing tools that quantitatively measure physical activity and sedentary behavior–related self-efficacy of pre- and in-service ECEs. Seven online databases were searched for original, peer-reviewed, English-written journal articles. Articles were deemed eligible if they employed a tool which measured physical activity and/or sedentary behavior–related self-efficacy of pre- or in-service ECEs. A total of 16 studies were included in this review, and 13 unique tools were identified. All tools measured task self-efficacy ( n = 13), while only 1 tool measured barrier self-efficacy, and approximately half of the tools ( n = 7; 54%) reported on the validity and reliability. Great variability existed among the self-efficacy items included in the tools; however, common constructs included: teaching/leading physical activity, fundamental movement skill development, and physical activity programming. Very few tools mentioned sedentary behavior ( n = 2) and outdoor/risky play ( n = 2). Given the low number of studies that tested validity and reliability of their self-efficacy tools, the lack of consideration for barrier self-efficacy, and the paucity of tools that fully encompassed physical activity, sedentary behavior, and outdoor play considerations for ECEs, future research is needed to validate a new, reliable tool.
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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 | 0.024 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".