The Canadian Network for Research in Schizophrenia and Psychoses: A Nationally Focused Approach to Psychosis and Schizophrenia Research
Bibliographic record
Abstract
Schizophrenia and other psychotic disorders affect approximately 4% of the population (more than 1.5 million Canadians) and indirectly affect many more family members, friends and other supporters [1] [2]. Schizophrenia rates among the world’s top ten causes of disability-adjusted life-years [3], with most psychosocial deterioration in schizophrenia occurring in the first five years of onset, if not sooner. Since the age of onset of psychosis occurs in late adolescence or early adult years it has the potential to negatively affect the lives of many young Canadians, during a developmental window when they are developing the life skills and experiences needed for independent living. Due to the potential severity of the illness and chronicity starting at an early age, schizophrenia and other psychotic disorders have significant direct and indirect costs in terms of treatment, but also related to unemployment, suicide (5% rate [4]), physical illnesses, and overall reduced life expectancy (in 2004 alone, 374 Canadians died prematurely from schizophrenia [5]). In Canada the cost of schizophrenia has been estimated to be up to 10 billion dollars annually (from 7 billion in 2004 [5]). Ultimately, the personal, family and societal cost/burden of schizophrenia is significant. A Canadian report on the hypothetical impact of affecting the causes of schizophrenia by as little as 10% (for e.g. by targeting obstetric complications or environmental risk factors such as cannabis use), as well as improving remission by only 10% (by improving protective factors via treatment – see [6]), suggested that more than 12 000 Canadians who would otherwise have schizophrenia would be illness-free within less than 10 years [7]. Such impacts can only be achieved with quality Canadian research in the field.
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 | 0.058 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".