Fiabilité test retest et validité de construit de la version française de L'Échelle fonctionnelle des membres inférieurs (ÉFMI), partie II
Bibliographic record
Abstract
INTRODUCTION: The Lower Extremity Functional Scale was translated into Canadian French in 2006; the translated version was titled Échelle fonctionnelle des membres inférieurs (EFMI). The construct validity and reliability of the EFMI have not been examined. OBJECTIVE: To examine the test-retest reliability, internal consistency, and construct validity of the EFMI. METHODOLOGY: Thirty-four participants who had undergone surgery or had an injury to the lower limb were asked to fill out a demographic questionnaire and complete the EFMI after their initial assessment while hospitalized. They completed the EFMI a second time within 72 hours. The intra-class correlation coefficient (ICC) and the kappa coefficient were chosen to examine the test-retest reliability of the EFMI, and Cronbach's alpha coefficient was calculated to assess internal consistency. A factor analysis was conducted to examine construct validity by determining the number of constructs and their meanings. RESULTS: The ICC value was 0.92 (95% CI: 0.88-0.96), and Cronbach's alpha was 0.95 (95% CI: 0.91-0.99). The factor analysis revealed that all elements of the EFMI share one principal construct. CONCLUSION: The results demonstrate that the French-Canadian version of the EFMI is a unidimensional tool with excellent test-retest reliability.
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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 | 0.046 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".