Self-diagnosis in the age of social media: A pilot study of youth entering mental health treatment for mood and anxiety disorders
Bibliographic record
Abstract
AIM: To understand young adult patients' perspectives around the importance of mental health diagnoses and use of online material to self-diagnose. METHODS: Prior to first appointment with a clinician, 57 patients at First Episode Mood and Anxiety Program answered questions about viewing mental health content online. They were asked their opinions regarding the importance of a diagnosis and if they believed they had a diagnosis not previously given by a clinician. Participants also completed the Social Media Engagement Scale for Adolescents (SMES-A). RESULTS: All participants reported viewing mental health content online, and social media sites were more commonly viewed than academically-oriented sites. Value placed on diagnosis was correlated with frequency of viewing mental health content online. Most patients reported believing they had diagnoses that were not previously given by a clinician (i.e., a self-diagnosis). Of these, most indicated social media contributed to this belief. Self-diagnosis was correlated with frequency of viewing mental health content on YouTube as well as score on the SMES-A. CONCLUSIONS: Young adults seeking mental healthcare indicated that information gleaned from social media was often used to self-diagnose. A diagnosis was found to be important for youth seeking mental health treatment and social media use appeared to be an associated factor. This research highlights attitudes about social media and diagnosis in youth entering mental health treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".