Evolution Theory: An Overview of Its Applications in Psychiatry
Bibliographic record
Abstract
OBJECTIVES: (1) Describe the concept, mechanisms and outcome of evolution; (2) review the current topics in research and clinical psychiatry where evolutionary concepts are explicitly applied. METHODS: The authors reviewed relevant textbooks of evolution, evolutionary psychiatry/psychology and articles in scientific journals, and discussed these topics in a college course at McGill University School of Medicine, Montreal, Canada. RESULTS: (1) Most natural scientists agree that evolution has occurred in all living beings. However, the mechanisms and outcomes of evolution are controversial. (2) In the first three quarters of the 20th century, several authors provided theories about human psychology based on ethological concepts. The so-called evolutionary psychology/psychiatry developed more recently, and it explores the adaptive/nonadaptive features of psychopathology and mental disorders. In the 1990 s a concept of mental disorder (as a harmful dysfunction) based on evolutionary theory has been developed. CONCLUSIONS: Evolution is a pivotal concept in biology with relevant applications in psychiatry. We suggest encouraging the interaction between psychiatric educators and researchers in evolutionary psychiatry and biology in order to improve the education of psychiatric residents in this subject.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".