Physical Education-Based Interventions Contribute to the Development of Fundamental Movement Skills in Primary School-Aged Children: A Systematic Review
Bibliographic record
Abstract
Physical education (PE) programs are uniquely situated to promote the development of fundamental movement skills (FMS). The aim of this systematic review was to examine the elements of PE-based interventions globally for the development of FMS, as assessed through the test of gross motor development (TGMD). A systematic literature search was conducted. Two reviewers screened studies based on title and abstract, and again by full text, to assess the eligibility of the articles. Nineteen studies met all of the inclusion criteria. All studies reported significant intervention effects, with different approaches to reporting scores in the TGMD. Examination of the articles demonstrated that FMS are a global focus in PE programs and that interventions can take on different lengths and formats and still demonstrate improved FMS proficiency. Observation of TGMD demonstrated a need for future research to complete the entirety of the TGMD subtests and report complete scores. Primary observations of interventions included the duration of the intervention, the delivery of the intervention by teachers with training, and the breadth of interventions that may be useful. PE-based interventions should focus on quality PE rather than quantity, such as teaching strategies and interventions incorporated into the typical PE program, and emphasize FMS concepts.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".