French Translation and Validation of the e-HEALS and the sDHLI scales with Canadian Seniors
Bibliographic record
Abstract
Introduction: e-Health literacy, the ability to understand and use online health information, can be assessed by scales such as the e-Health Literacy Scale (eHeals) and the performance sub-scale of Digital Health Literacy Instrument (sDHLI). However, these scales are not available in French. Objective: To translate and validate the eHeals and the sDHLI from English into French. Methodology: A translation-back translation process involving six steps with independent translators was carried out. The final versions were validated through an online survey in Canada among Canadian seniors (>65 years). The internal consistencies of the translated scales were calculated, and the average e-health literacy scores between French-speaking and English-speaking individuals were compared. Results: A total of 1,000 Canadian seniors participated in the validation, predominantly male (54.6%), white (90.6%), English-speaking (62.9%), with a university level of education (42%). The study achieved a 100% completion rate, as all 1,000 participants fully completed the survey without any dropouts or missing data. The translated scales showed internal consistencies of 0.88 for eHeals and 0.40 for the sDHLI. Significant differences were observed between the average scores of English-speaking and French-speaking respondents for the sDHLI subscale (p < 0.0001), but not for the eHeals (p = 0.4) Conclusion: Validated French versions of the scales have been produced and culturally adapted. The differences in scores between respondents demonstrate the importance of cultural adaptation in the translation of measurement scales.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".