Analysis of instagram profiles found through inscriptions on the walls of the In – Patient Adolescent Psychiatry Unit at the University Hospital in Kraków, Poland
Bibliographic record
Abstract
Aim of the study Adolescents are increasingly active in social media: 72% use Instagram while as many as a quarter suffer from at least one mental disorder, Internet users among them. A number of studies confirming the mutual influence of social media and mental health have been conducted but there is a shortage of data on the Internet activity of people suffering from mental disorders. This study aims at extending the existing knowledge by analyzing Instagram accounts of adolescent psychiatric in-patients. Subject or material and methods We analyzed the contents of Instagram accounts, links to 36 of which were hidden in graffiti drawn by patients on the walls of an inpatient adolescent psychiatric ward. After excluding inactive and nonexistent accounts, 21 addresses were analyzed with respect to the number and content of published posts and comments left under them. Results 90% of the accounts belonged to girls. 52% revealed the owner’s identity. The posts were mainly depressive, which correlated with the psychopathology of the patients. The comments differed in number and in character depending on the content of the post: replies to posts related to body image were mainly supportive, while comments on posts related to self-harm mainly expressed sympathy. Discussion Most of the analyzed Instagram posts are related to the typical psychopathology of the patients hospitalized on our ward. In addition, it is also similar to negative effects that social media may have on mental health. Conclusions The association found in the study show that conducting further research on social media use by psychiatric patients may be clinically important.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".