Potential use of Internet-based screening for anxiety disorders: a pilot study
Bibliographic record
Abstract
BACKGROUND: The Internet is a widely used resource for obtaining health information. Internet users are able to obtain anonymous information on diagnoses and treatment, seek confirmatory information, and are able to self-diagnose. We posted a self-report diagnostic screening questionnaire for DSM-IV anxiety and mood disorders (MACSCREEN) on our clinic website. METHOD: Three hundred and two individuals completed the MACSREEN. For those who qualified for a DSM-IV disorder, self-report symptom severity measures were completed for the specified disorder: Quick Inventory of Depressive Symptomatology, self-report, Social Phobia Inventory, GAD-7, Davidson Trauma Scale, Panic and Agoraphobia Scale, and Yale/Brown Obsessive Compulsive Scale, self-report. Cutoff scores for each self-report measure were used to evaluate clinically significant symptom severity. Respondents were also asked to complete a series of questions regarding their use of the Internet for health information. RESULTS: The mean age of the MACSCREEN sample was 35.2 years (±13.9), where the majority (67.2%) were female. The most frequently diagnosed conditions were social phobia (51.0%), major depressive disorder (32.4%), and generalized anxiety disorder (25.5%). Sixty-five percent of the sample met criteria for at least one disorder. Most respondents reported completing the MACSCREEN, as they were concerned they had an anxiety problem (62.3%). The majority of respondents reported seeking health information concerning specific symptoms they were experiencing (54.6%) and were planning to use the information to seek further assessment (60.3%). CONCLUSION: Individuals with clinically significant disorder appear to be using the Internet to self-diagnose and seek additional information.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".