mHealth App Usability Questionnaire for Stand-Alone mHealth Apps Used by Health Care Providers: Canadian French Translation, Cross-Cultural Adaptation, and Validation (Part 1) (Preprint)
Bibliographic record
Abstract
BACKGROUND An increasing number of health care professionals are using mobile apps. The mHealth App Usability Questionnaire (MAUQ) was designed to evaluate the usability of mobile health apps by patients and providers. However, this questionnaire is not available in French. OBJECTIVE This study aims to translate (from English to Canadian French), cross-culturally adapt, and initiate the validation of the original version of MAUQ for stand-alone mobile health apps used by French-speaking health care providers. METHODS A cross-cultural research study using a well-established method was conducted to translate MAUQ to Canadian French by certified translators and subsequently review it with a translation committee. It was then back translated to English. The back translations were compared with the original by the members of the committee to reach consensus regarding the prefinal version. A pilot test of the prefinal version was conducted with a sample of 49 potential users and 10 experts for content validation. RESULTS The statements are considered clear, with interrater agreement of 99.14% among potential users and 90% among experts. Of 21 statements, 5 (24%) did not exceed the 80% interrater agreement of the experts regarding clarity. Following the revisions, interrater agreement exceeded 80%. The content validity index of the items varied from 0.90 to 1, and the overall content validity index was 0.981. Individual Fleiss multirater κ of each item was between 0.89 and 1, showing excellent agreement and increasing confidence in the questionnaire’s content validity. CONCLUSIONS This process of translation and cultural adaptation produced a new version of MAUQ that was validated for later use among the Canadian French–speaking population. An upcoming separate study will investigate the psychometric properties of the adapted questionnaire.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".