A rise in social media use in adolescents during the COVID-19 pandemic: the French validation of the Bergen Social Media Addiction Scale in a Canadian cohort
Bibliographic record
Abstract
INTRODUCTION: Social media use has grown dramatically since its inception in the early 2000s and has further increased during the COVID-19 pandemic. Problematic use of social media (PUSM) is a type of behavioural addiction which has generated increasing interest among mental health clinicians and scholars in the last decade. PUSM is associated with multiple psychiatric conditions and is known to interfere with patients' daily functioning. There is no single accepted definition of PUSM, nor means of measuring it, in the literature. The Bergen Social Media Addiction Scale (BSMAS) is a helpful tool for identifying PUSM. This paper aims to validate BSMAS and to translate it from English into French, with the goal of making this clinical screening tool for PUSM available in French-language contexts. METHOD: This study explored the psychometric validity of the French version of the BSMAS in a sample of 247 adolescents, who were either psychiatric inpatients (the hospitalized group, n = 123) or recruited in local high schools (the community group, n = 124). RESULTS: The adolescents in the sample reported an increase in their social media use during the COVID-19 pandemic. This increase was more pronounced in the hospitalized group. Confirmatory factorial analysis showed an excellent fit, very good internal consistency and established convergent validity for the French version of the BSMAS. A total of 15.4% of the hospitalization group and 6.5% of the community group met the recommended clinical cutoff of 24 on the BSMAS, suggesting problematic use of social media. CONCLUSIONS: The French version of BSMAS is a psychometrically validated and clinically useful tool to screen for PUSM in adolescents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 0.000 |
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".