Wrongful Medicalization and Epistemic Injustice in Psychiatry
Bibliographic record
Abstract
In this paper, my goal is to use an epistemic injustice framework to extend an existing normative analysis of over-medicalization to psychiatry and thus draw attention to overlooked injustices. Kaczmarek (2019) has developed a promising bioethical and pragmatic approach to over-medicalization, which consists of four guiding questions covering issues related to the harms and benefits of medicalization. In a nutshell, if we answer “yes” to all proposed questions, then it is a case of over-medicalization. Building on an epistemic injustice framework, I will argue that Kaczmarek’s proposal lacks guidance concerning the procedures through which we are to answer the four questions, and I will import the conceptual resources of epistemic injustice to guide our thinking on these issues. This will lead me to defend more inclusive decision-making procedures regarding medicalization in the DSM. Kaczmarek’s account complemented with an epistemic injustice framework can help us achieve better forms of medicalization. I will then use a contested case of medicalization, the creation of Premenstrual Dysphoric Disorder (PMDD) in the DSM-5 to illustrate how the epistemic injustice framework can help to shed light on these issues and to show its relevance to distinguish good and bad forms of medicalization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".