Bibliographic record
Abstract
The debate on the role of culture on psychiatric epidemiology has evolved considerably in the past two decades. There is now a general consensus that the integration of the universalist and culturally relativist approaches, and their methodologies, is required to generate a truly international psychiatric epidemiology. The large body of research investigating the influence of culture on the epidemiology of depression has produced a number of key findings: the clinical presentation of depression in all cultures is associated with multiple somatic symptoms of chronic duration; psychological symptoms, however, are important for diagnosis and can be easily elicited. The diagnostic differentiation between depression and anxiety in general health care settings is not clinically valid. Culturally appropriate terminology for depression can be identified and their use may improve levels of recognition and treatment compliance. It is also evident that culture is only one factor in the difference between, and within, human societies which has a bearing on the epidemiology of depression. Other factors, which may interact with culture, such as gender and income inequality, are major risk factors for depression. Future international research must focus on two themes: (i) intervention studies including cost-effectiveness outcomes; and (ii) research aiming to bridge the gap between regional public health priorities and the concern that psychiatrists have about depression.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".