Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: Social media holds promise for expanding the reach of mental health services, especially for young people who frequently use these popular platforms. We surveyed social media users who self-identified as having a mental illness to learn about their use of social media for mental health and to identify opportunities to augment existing mental health services. METHODS: We asked 240 Twitter users who self-identified in their profile as having a mental illness to participate in an online survey. The survey was in English and inquired about participants' mental health condition, use of social media for mental health and interest in accessing mental health programs delivered through social media. RESULTS: Respondents from 10 countries completed 135 surveys. Most respondents were from the United States (54%), Canada (22%) and the United Kingdom (17%) and reported a psychiatric diagnosis of either schizophrenia spectrum disorder (27%), bipolar disorder (25%), major depressive disorder (16%) or depression (20%). Young adults age ≤35 (46%) were more likely to use Instagram (P = .002), Snapchat (P < .001) and their mobile phone for accessing social media (P < .001) compared to adults age 36 and older (53%). Most participants (85%) expressed interest in mental health programs delivered through social media, especially to promote overall health and wellbeing (72%) and for coping with mental health symptoms (90%). CONCLUSIONS: This exploratory study demonstrates the feasibility of reaching social media users with mental illness and can inform efforts to leverage social media to make evidence-based mental health services more widely available to those in need.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it