Une version canadienne française du Lower Extremity Functional Scale (LEFS) : L'Échelle fonctionnelle des membres inférieurs (ÉFMI), partie I
Bibliographic record
Abstract
INTRODUCTION: The Rehabilitation Network of Ottawa-Carleton (RENOC) recommends that its institutions use the Lower Extremity Functional Scale (LEFS). Since RENOC serves both Francophone and Anglophone clients, the LEFS needed to be translated to assess the Francophone clientele. OBJECTIVE: To create a French-Canadian version of the LEFS, with the proposed title "Échelle fonctionnelle des membres inférieurs" (ÉFMI), and to examine its content validity. METHODOLOGY: A modified version of Vallerand's (1989) cross-validation technique for questionnaires was used to validate the equivalence of the French version of the LEFS. First, a parallel reverse translation of the LEFS was completed. Second, a committee of experts examined the two drafts and created a first experimental version of the ÉFMI. Third, this version was evaluated by a second committee of experts. Following the comments received, a second experimental version was created. Fourth, 35 rehabilitation professionals evaluated the second experimental version of the ÉFMI on a scale of ambiguity and proposed final changes. RESULTS: On the final version of the ÉFMI, the statements demonstrated a relatively low average level of ambiguity, ranging between 1 and 2.54. CONCLUSION: The rigorous four-step process followed in this study produced a French-Canadian version of the LEFS with demonstrated content validity.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.010 | 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".