Identifying Criteria for a Physical Literacy Screening Task: An Expert Delphi Process
Bibliographic record
Abstract
Objectives: REACH (Recreation, Education, Allied-health, Coaching, Healthcare) leaders support children's physical literacy journey in diverse settings.This project sought physical literacy screening tool criteria that REACH leaders could use to assess children.Methods: A 3-round expert Delphi process sought consensus (75% of participants stating agree/strongly agree) regarding physical literacy screening.Group discussions (Round 1) identified screening issues.Qualitative analyses represented the issues as statements.Experts rated each statement (5-point Likert scale) in Rounds 2 and 3. Mean Round 2 rating for each statement was provided in Round 3.Results: 53 experts were invited to participate with 37 (63% female, mean career length = 16 years) providing consent.Each round comprised at least 7 experts with primary/secondary expertise for each sector.Round 1 identified 60 criteria and 27 potential screening tasks, which were represented in 90 statements.Consensus was achieved for 44/90 statements in Round 2 and 51/90 statements in Round 3. Conclusions:Expert consensus suggests that physical literacy screening should utilize both objectively measured tasks and questionnaires.Encompassing multiple facets of physical literacy, including motor competence, motivation, strength, endurance, and daily behavior, is important.Research is required to identify potential tasks that meet these criteria and are suitable for each REACH sector.
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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 | 0.082 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".