Bibliographic record
Abstract
Five years ago, Jarvis' heralded the rebirth of investigation of the social causation of psychoses.In this In Review section he charted the growing interest in Europe based on studies of immigrant groups.2 Evidence was accumulating that social factors have a role in the development of schizophrenia and other psychoses.1,3At that time, the mechanisms by which social factors exert their influence were unknown. It was hoped that future research would identify how the exposure to social adversity leads to the development of psychotic symptoms.1The literature has grown significantly, but researchers investigating the social causes of psychosis are still in the minority.4 This may be changing as clear and plausible mechanisms through which social factors can cause the cognitive, structural, and neurochemical changes seen in the psychoses are being reported.5,6The argument that psychosis is a brain disease does not rule out social causation.5,7It may be worth reflecting on that we have no problem accepting the social causation of other illnesses with physical manifestations, such as coronary thrombosis.The final common pathway is a blockage of the artery leading to heart muscle death. This can occur by a slow plugging of the arteries, an embolus being thrown offa fatty plaque or spasm. Fatty plaques build up over many years from childhood onwards. The actual timing of the heart attack not only depends on a person's lifetime accumulation but also the work that the heart has to do at the time; heart attacks usually occur during exercise. The amount of work is linked to individual factors, such as weight, as well as environmental factors, such as the ambient temperature, but these are not the only social risk factors. We all realize that risk is complex. Though inheritance plays a role (and for some with familial hyperlipidemias it is a significant role), for most it is a vulnerability factor, and whether we have a heart attack relies on a combination of other factors including our diet and level of exercise, which are influenced by our social contexts. For instance, our diet depends on what types of food are available and what we can afford, but we know there are other factors involved, such as our psychological state, as we tend toward eating high-calorie foods when we are stressed. Other responses to stress are the use of anxiolytics, such as tobacco and alcohol, to ease social interactions. These may increase our risk of cardiac disease. Their rate of use in society depends not only on culture but also on their availability and cost. The last 2 factors are regulated by the state, and the sources of stress that lead to these behaviours are similarly complex, including financial insecurity, job stress, family and relationship stress, and our position in society.8I could go on.The point is that clinicians, researchers, and the general public are very quickly able to build and understand complex models of risk for physical diseases. They understand that there are 4 dimensions of risk: vulnerabilities and resiliency factors at an individual level; environmental-level factors, such as where you live, what the laws are, and how easy it is to live a healthy lifestyle; an interaction can occur because individual risks and environmental risks are not independent (for instance, your social context changes if you lose a parent when you are young); and, finally, timing is important because there are sensitive periods in development and resiliency changes with age.7The model for social risk for schizophrenia and other psychoses is similar to that of heart disease. The 4-dimensional model used to explain the social causation of heart attacks was actually developed to explain the causation of schizophrenia for public health officials.7 There are individual factors, such as genetic vulnerability, that are important. There are group level factors, such as where you live and the position of your group in society. …
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".