French-Canadian translation of the WheelCon-M (WheelCon-M-F) and evaluation of its validity evidence using telephone administration
Bibliographic record
Abstract
PURPOSE: The objectives of this study were to: (1) translate the Wheelchair Use Confidence Scale for Manual Wheelchair Users (WheelCon-M) into a French-Canadian version (WheelCon-M-F); and (2) evaluate the WheelCon-M-F validity evidence based on response processes, internal structure, and relations with other variables. METHODS: The WheelCon-M was translated from English to French using the Translation and Cultural Adaptation of Patient Reported Outcomes Measures - Principles of Good Practice guidelines. We used a test-retest design to examine the validity of the WheelCon-M-F with 24 community dwelling, experienced manual wheelchair users who had a variety of musculoskeletal and neurological diagnoses. RESULTS: The mean ± SD WheelCon-M-F score was 63.8 ± 19.9. All WheelCon-M-F items were either identical or similar in meaning to the WheelCon-M items. Clarification issues were identified with 27/63 items. Cronbach's alpha was 0.98 and the retest intraclass correlation coefficient was 0.87. The standard error of measurement and smallest real difference were 7.2 and 19.9, respectively. There were no floor or ceiling effects. WheelCon-M-F correlations with social support and participation were r = 0.54 and 0.78, respectively. CONCLUSIONS: The WheelCon-M-F is a valid outcome measure for assessing manual wheelchair confidence in the French-Canadian population. IMPLICATIONS FOR REHABILITATION: The WheelCon-M-F is a valid outcome measure available for assessing wheelchair confidence, a modifiable barrier to wheelchair use. Translation of the WheelCon-M into the WheelCon-M-F allows collection of both clinical and research wheelchair confidence data using the two official Canadian languages, English and French.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".