Faculty Opinions recommendation of Are consumers of Internet health information "cyberchondriacs"? Characteristics of 24,965 users of a depression screening site.
Bibliographic record
Abstract
BACKGROUND: The number of individuals looking for health information on the Internet continues to expand. The purpose of this study was to understand the prevalence of major depression among English-speaking individuals worldwide looking for information on depression online.METHODS: An automated online Mood Screener website was created and advertised via Google AdWords, for 1 year. Participants (N = 24,965) completed a depression screening measure and received feedback based on their results. Participants were then invited to participate in a longitudinal mood screening study.RESULTS: Of the 24,965 who completed the screening, 66.6% screened positive for current major depression, 44.4% indicated current suicidality, and 7.8% reported a recent (past 2 weeks) suicide attempt. Of those consenting to participate in the longitudinal study (n = 1,327 from 86 countries), 77.4% screened positive for past depression, 64.6% reported past suicidality, and 17.5% past suicide attempt. Yet, only 25% of those screening positive for current depression, and only 37.2% of those reporting a recent suicide attempt are in treatment.CONCLUSION: Many of the consumers of Internet health information may genuinely need treatment and are not "cyberchondriacs." Online screening, treatment, and prevention efforts may have the potential to serve many currently untreated clinically depressed and suicidal individuals.© 2011 Wiley-Liss, Inc. PMID: 21681872 Funding information This work was supported by: NIMH NIH HHS, United States Grant ID: K08 MH091501-01 NIMH NIH HHS, United States Grant ID: 5K08MH091501 NIMH NIH HHS, United States Grant ID: K08 MH091501 NIMH NIH HHS, United States Grant ID: T32 MH19391 NIMH NIH HHS, United States Grant ID: T32 MH019391
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
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".