Social media, body image and mental health: a bibliometric analysis
Bibliographic record
Abstract
The relationship between social media, body image and mental health has been gaining attention. The present study aimed to map, through bibliometric parameters, the development of scientific productions that relate social media, body image and mental health. The research was carried out in the PubMed database and bibliometric indicators were analysed using the Bibliometrix statistical package, available in R language. We analysed 11.132 articles dated from 1982 to 2024, published in 1.799 journals and authored by 38.290 researchers. Over time, publications had an annual growth of 5.08% and a total of 22.92% of international collaborations. The Journal of Medical Internet Research stands out with the largest number of publications (n = 1,184) and the University of Toronto (n = 845) and researcher Helen Christensen (n = 141) with the largest number of published works. The analyses demonstrated interest in studying populations of different ages, with emphasis on research with women. Preponderant collaboration was identified between the United States, China, Canada and Italy, as well as between Germany, Sweden and Switzerland. It is concluded that research related to the topics investigated is gaining more space in publications, as well as international interest in its study, discussion and understanding.
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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 | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.030 | 0.077 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".