“Clinician Knows Best”? Injustices in the Medicalization of Mental Illness
Bibliographic record
Abstract
This paper uses a non-ideal theory approach advocated for by Alison Jaggar to show that practices involved with the medicalization of serious mental disorders can subject people who have these disorders to a cycle of vulnerability that keeps them trapped within systems of injustice. When medicalization locates mental disorders solely as problems of individual biology, without regard to social factors, and when it treats mental disorders as personal defects, it perpetuates injustice in several ways: by enabling biased diagnoses through stereotyping, by exploiting and coercing people who are seen as insufficiently competent, and by perpetuating idealized conceptions of choice and control that do not take into account people’s real limitations and the social context of health. Through practices of diagnosis, treatment, and recovery, medicalization can perpetuate injustices toward people who have serious mental disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".