Impact of dualism on the perception of treatability in psychiatry
Bibliographic record
Abstract
Background: A false division between mental and physical disorders is supported by dualism, contributing to mental health stigma. There is a widespread misconception about the prognosis and treatment options for psychiatric diseases. This is despite data supporting the effectiveness of psychiatric treatments for a variety of illnesses that have been proven by meta-analysis. In general, the efficacy of drugs used to treat physical problems and psychiatric disorders is comparable. Methods: In this article, experts from a variety of fields—including psychiatry, primary care, and general medicine—highlight how the paradigms based on dualism play a crucial role in maintaining the myths regarding psychiatric disorders, particularly those that relate to their treatability in comparison to physical health conditions. Results: There are numerous similarities between mental and physical problems in terms of the causes andtreatment. Healthcare, like other complex human systems, is rife with uncertainty. In actuality, the severity and treatability of both physical and mental diseases range widely. Treatment response varies from person to person. There are certain physical and mental health disorders that respond well to treatment, some that do not, and some for which there are currently no effective cures. Conclusion: We believe that dualism, which promotes the separation of mental and physical phenomena, is the core driving force behind these misconceptions. These fallacies, in our opinion, are primarily motivated by dualism, which advocates the division of mental from physical occurrences.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.019 |
| 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".