Is The Functional Movement Screen A Valid Indicator Of Joint Mobility? A Construct Validity Study.
Bibliographic record
Abstract
PURPOSE: To examine the associations between FMS Deep Squat (SQT) task scores and passive ankle dorsiflexion, hip flexion, and shoulder flexion range-of-motion (ROM) capacity in student-athletes. It was hypothesized that athletes who scored higher on the SQT task would have greater ROM capacity than would those who scored lower. METHODS: One-hundred one varsity basketball, volleyball, ice hockey, and soccer athletes were recruited from a university population. Licensed therapists used a manual goniometer to measure athletes’ ankle dorsiflexion (with knee flexed), hip flexion (with knee flexed), and shoulder flexion (with elbow extended) ROM capacity (left- and right-side averaged). Athletes performed the SQT on the same day that ROM measurements were taken, and SQT scores were assigned by trained observers using published criteria. Associations between SQT task scores and ROM capacities were assessed using medians with inter-quartile ranges (IQRs) and analyses of variance. RESULTS: As the table of ROM data shows, there were no statistically significant differences in hip and shoulder flexion ROM capacity between athletes who scored one, two, and three on the SQT (one participant with a SQT score of 0 excluded). Athletes who scored one on the SQT had lower ankle ROM capacity than those who scored two or three. CONCLUSIONS: The SQT may be useful as a low-cost and expedient tool to identify athletes with passive ankle dorsiflexion restrictions, but there was little evidence of its construct validity as a general indicator of joint ROM capacity in student-athletes. Further study of the measurement properties of the FMS is warranted, especially given its widespread use.Table: No title available.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".