Bibliographic record
Abstract
There's no scientific evidence that city life makes people more depressed than rural life, but schizophrenia rates do differ significantly between the 2 settings, a recent International Conference on Urban Health was told. In the past 10 years, major birth cohort studies in developed countries have revealed that the incidence of schizophrenia is about 2 times higher among people in cities, reported Dr. Ezra Susser, head of epidemiology at Columbia University's Mailman School of Public Health. “It's not clear if it is birth in cities, or upbringing in cities, but there is something about city living that increases risk,” he said. Where you are born and brought up is a larger contributing factor to risk than genetic predisposition. Indeed, 34.6% of cases would be prevented if people were not born and brought up in cities, compared to 5.4% of cases that would be prevented if people did not have parents or siblings who suffered from the illness, Susser told participants at the New York conference. The higher rate in urban areas may be due to environmental toxins, the social context that people live in, and contagion, including prenatal infections. The studies also reveal a “dose response”: the more urban the setting, the higher the risk. “This is one of the most solid findings in schizophrenia today,” said Susser. But the association with urban living has not received enough attention because current research centres on neural imaging and pharmacology, Susser says. — Ann Silversides, Toronto
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".