Bergen social media engagement and experiences scale (Be-SMEE): A short questionnaire covering important aspects of social media use among adolescents. Development and association with symptoms of depression and anxiety.
Bibliographic record
Abstract
<ns3:p>Background There is a need to go beyond mere measures of time used on social media. Existing tools inadequately capture the multidimensional nature of social media use, leaving a gap for concise yet comprehensive assessment tools. Aims This study aimed to develop a short questionnaire addressing three critical dimensions of social media use: self-presentation, negative experiences, and problematic use. The association between these dimensions and symptoms of anxiety and depression was also investigated. Methods This study uses two independent datasets of adolescents aged 16+ years in Norway. Using Ant Colony Optimization (ACO) analyses, a pool of 31 social media items was analyzed to investigate factor structure and associations with symptoms of anxiety and depression. For model development, the “LifeOnSoMe”-study was employed (>3,500 participants), and data from a pilot study (~500 participants) was used for external validation. Results Based on ACO-analyses, a 20-item six-factor model was identified, encompassing social comparison and self-presentation (five items), and three items for each of the following domains: negative experiences (Negative acts and Unwanted attention from others) and problematic use (Subjective overuse, Social obligations, and Source of concern). Confirmatory factor analyses demonstrated very good to excellent fit in both datasets, and consistent associations between the six different domains and symptoms of anxiety and depression. Discussion The proposed 20-item questionnaire captures six important aspects of social media use, and it may serve as a meaningful tool for assessing the potential association between social media use and mental health and related outcomes.</ns3:p>
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".