French Versions of 2 English Questionnaires on Problematic Digital Use Assessed by Adolescents and Their Parents: Cross-Cultural Linguistic Translation and Adaptation Study
Bibliographic record
Abstract
Background: The emergence of problematic digital use is increasingly alarming, affecting between 7% and 20% of the world's adolescent population. However, there is no validated questionnaire in French to measure this. Only a few questionnaires, either self-reported by adolescents or hetero-reported by parents, have been translated and validated in English. Objective: This study aims to translate into French the Digital Addiction Scale for Children (DASC), which is self-reported by adolescents, and the Problematic Media Use Measure (PMUM), which is hetero-reported by parents of adolescents. Methods: We used the "forward and backward" method to establish the translation and achieve cross-cultural adaptation with 8 parents and 8 adolescents aged between 12 and 17 years. There were three stages: (1) initial translation and synthesis or reconciliation of the translations phase; (2) back translation and expert committee phase; and (3) pretesting phase, during which 8 parents completed the PMUM questionnaire and 8 adolescents completed the DASC questionnaire. Results: Despite slight variations in translation for both questionnaires, the translators quickly reached a consensus during the translation phase. The expert committee did not propose any other conceptual changes. In the final phase, the parents made no comments to improve the questions or the wording. Although some adolescents mentioned repetition between certain questions, they did not suggest any improvements to the DASC questionnaire in French. Conclusions: Although the translated versions of the DASC and PMUM questionnaires provide a foundation for detecting problematic digital use, they require further validation studies to confirm their reliability and applicability in the French adolescent population.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".