Are diverse factors proxies for architectural influences? A case for architecture in the aetiology of schizophrenia
Bibliographic record
Abstract
Introduction The last half-century of epidemiological enquiry into schizophrenia can be characterized by the search for neurological imbalances and lesions for genetic factors. The growing consensus is that these directions have failed, and there is now a growing interest in psychosocial and developmental models. Another area of recent interest is in epigenetics – the multiplication of genetic influences by environmental factors. Methods This integrative review comparatively maps current psychosocial, developmental and epigenetic models for schizophrenia epidemiology to identify crossover and theoretical gaps. Results In the flood of data that is being produced around the schizophrenia epidemiology, one of the most consistent findings is that schizophrenia is an urban syndrome. Once demographic factors have been discounted, between one-quarter and one-third of all incidence is repeatedly traced back to urbanicity – potentially threatening more established models, such as the psychosocial, genetic and developmental hypotheses. Conclusions Close analysis demonstrates how current models for schizophrenia epidemiology appear to miss the mark. Furthermore, the built environment appears to be an inextricable factor in all current models and indeed may be a valid epidemiological factor on its own. The reason the built environment hasn’t already become a de rigueur area of epidemiological research is possibly trivial – it just doesn’t attract enough science, and lacks a hero to promote it alongside other hypotheses.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".