Bibliographic record
Abstract
Depression can be a private disorder that goes unrecognized and untreated. Fortunately, the Internet has literally hundred of online resources for these patients, including a couple that help people assess the extent of their problem. The Internet Mental Health site (www.mentalhealth.com) is a recognized online leader in this area. Created by Vancouver psychiatrist Phillip Long, the site has received numerous awards from professional and Web-based organizations. The site describes itself as an encyclopedia of mental health information and includes information and descriptions of various disorders, notes about various mental health drugs and an online magazine of current news and research. One of the more interesting sections includes an online diagnosis tool; various questionnaires cover many disorder areas, including depression. It can either be taken by the patient or administered by a clinician. In each case it delivers a basic diagnosis and treatment plan. The site stresses that this is only a tool: ”This program is offered by Internet Mental Health as an aid to diagnosis and is not a substitute for expert clinical judgement by a licensed mental health professional.” The US-based National Mental Health Association has a similar tool (www.depression-screening.org) that offers plenty of information about various depressive disorders and includes a self-directed screening tool. Shela Halper, vice-president of public education at the NMHA, stresses that this online service is not meant to be the only tool used to identify the problem. ”It is important that people understand this Web site can not and should not substitute for a visit to their doctor or a qualified mental health professional,” states Halper. ”It is designed only to be a first step. While screening tests can help uncover the symptoms, they can not provide a diagnosis for depression.” Finally, for patients wanting interaction with others, there are online discussion groups in the ALT.SUPPORT .DEPRESSION and SOC .SUPPORT .DEPRESSION areas of Usenet. —
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.138 | 0.041 |
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".