Factor structure, stability, and congruence in the functional movement screen
Bibliographic record
Abstract
The scoring scheme for the functional movement screen implicitly assumes that the factor structure is consistent, stable, and congruent across different populations. To determine if this is the case, we compared principal components analyses of three samples: a healthy, general population (n = 100), a group of varsity athletes (n = 101), and a group of firefighters (n = 397). The congruence of the principal components derived through these analyses were evaluated across all of the samples, using Tucker’s congruence coefficient. Factor extraction was guided by parallel analyses, and interpretation was facilitated by Varimax rotation. We observed factor instability, low factor congruence, and inconsistent factor structure. Additionally, we observed a two-factor structure of the functional movement screen in all of our sample groups. These analyses suggest that, although the independent elements of the functional movement screen may continue to be used, use of the composite score is not supported by the factor structure of the measure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".