Spanish Version of the System Usability Scale for the Assessment of Electronic Tools: Development and Validation
Bibliographic record
Abstract
BACKGROUND: The System Usability Scale (SUS) is a common metric used to assess the usability of a system, and it was initially developed in English. The implementation of electronic systems for clinical counseling (eHealth and mobile health) is increasing worldwide. Therefore, tools are needed to evaluate these applications in the languages and regional contexts in which the electronic tools are developed. OBJECTIVE: This study aims to translate, culturally adapt, and validate the original English version of the SUS into a Spanish version. METHODS: The translation process included forward and backward translation. Forward translations were made by 2 native Spanish speakers who spoke English as their second language, and a backward translation was made by a native English speaker. The Spanish SUS questionnaire was validated by 10 experts in mobile app development. The face validity of the questionnaire was tested with 10 mobile phone users, and the reliability testing was conducted among 88 electronic application users. RESULTS: The content validity index of the new Spanish SUS was good, as indicated by a rating of 0.92 for the relevance of the items. The questionnaire was easy to understand, based on a face validity index of 0.94. The Cronbach α was .812 (95% CI 0.748-0.866; P<.001). CONCLUSIONS: The new Spanish SUS questionnaire is a valid and reliable tool to assess the usability of electronic tools among Spanish-speaking users.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".