Validating the dance fundamental movement skills assessment for balance
Bibliographic record
Abstract
The Dance Fundamental Movement Skills Assessment (DFMS-A) examines fundamental movement skills as described by Footprints Dance Project in their teaching resource: Footprints Movement Tool. It was designed to inform teaching practice, highlighting students’ strengths and weaknesses. In addition to providing holistic assessment for the whole dancer, the DFMS-A provides an extended score range encompassing the higher level of corporal expectation demanded of dancers, as opposed to pass/fail metrics (normal/abnormal). The current study aims to evaluate the validity and reliability of the balance component of the DFMS-A using a sample population from the University. The DFMS-A was compared with three standard physical assessments of balance: Y balance test, Single Leg Stance (SLS) test, and Functional reach test. Pearson Correlations demonstrated significant correlations between the DFMS-A and Y balance and SLS tests respectively (r = 0.745–0.974; p < 0.008), with minimal correlation found with the Functional Reach test. Intra-rater reliability was tested 1.5 years after the original assessment was completed, and demonstrated high reliability (>85%). These positive results support the use of the DFMS-A as a comprehensive balance assessment with more depth than current tests, suggesting it may warrant further testing in other aspects of functional movement such as weight transfer and body alignment.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".