Cross-sectional survey of users of Internet depression communities
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
BACKGROUND: Internet-based depression communities provide a forum for individuals to communicate and share information and ideas. There has been little research into the health status and other characteristics of users of these communities. METHODS: Online cross-sectional survey of Internet depression communities to identify depressive morbidity among users of Internet depression communities in six European countries; to investigate whether users were in contact with health services and receiving treatment; and to identify user perceived effects of the communities. RESULTS: Major depression was highly prevalent among respondents (varying by country from 40% to 64%). Forty-nine percent of users meeting criteria for major depression were not receiving treatment, and 35% had no consultation with health services in the previous year. Thirty-six percent of repeat community users who had consulted a health professional in the previous year felt that the Internet community had been an important factor in deciding to seek professional help. CONCLUSIONS: There are high levels of untreated and undiagnosed depression in users of Internet depression communities. This group represents a target for intervention. Internet communities can provide information and support for stigmatizing conditions that inhibit more traditional modes of information seeking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 it