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 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".