Bibliographic record
Abstract
Schizophrenia affects nearly 300 000 Canadians, or almost 1% of the population, but Tony Cerenzia, president of the Schizophrenia Society of Canada, says it remains one of the most widely misunderstood and feared illnesses. “The lingering stigma associated with schizophrenia too often results in discrimination and, consequently, reluctance to seek appropriate treatment,” he says. The Internet is providing new ways to help break down some of the barriers facing these patients. One of the primary online sources of information is the Schizophrenia Society of Canada's own site (www.schizophrenia.ca), which offers resources for both the patient and professional, including access to various reports, studies and tools. There is an impressive list of vetted resources, along with a collection of contacts for FPs who treat schizophrenic patients. It includes names, telephone numbers and email addresses of people who are available to help manage patients with schizophrenia and related disorders across the country. Another useful Canadian resource is Internet Mental Health (www.mentalhealth.com), which is run by Vancouver-based psychiatrist Phillip Long. It provides information for patients, caregivers and health care professionals on a wide range of disorders, including schizophrenia. For those recently diagnosed with the illness, a good place to start is the US National Institute of Mental Health's site (www .nimh .nih .gov /publicat /schizoph .htm), which has a general introduction to the disease that is suitable for lay people. Finally, the CMA offers online resources for physicians and other health care providers. The Journal of Psychiatry & Neuroscience (www .cma.ca/jpn), published by the CMA, includes links to recent articles and research abstracts dealing with the treatment of schizophrenia. — Michael OReilly, ten.ylliero@ekim
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.697 | 0.515 |
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".