From distress to illness: a critical analysis of medicalization and its effects in clinical practice
Bibliographic record
Abstract
Accessible summary Construct validity of ‘mental illness’ remains elusive and has been replaced by the more manageable problem of reliability. Health‐care professionals may reliably diagnose similar clusters of symptoms but, realistically speaking, these conditions may not be mental disorders. Labelling individuals as mentally ill only accentuates the burden of disease by situating the problem within the person, rather than to engage in the difficult task of addressing the contextual elements that may be at the source of distress. Drawing on research findings in the field of HIV/AIDS nursing, we can see the impact of psychiatric pathologization of women who experience treatment‐induced body changes and subsequently, must accept that their response to these changes become the target of psychiatric labelling and interventions. We believe that health‐care providers first need to challenge the idea that distress associated with lipodystrophy should result in pathologization. We argue that nurses should be at the forefront of such an initiative because they have the professional responsibility to act as patient advocates. Abstract This paper is precisely aimed at exposing the limits of psychiatry's scientific claim‐making and exploring how psychiatric diagnoses come to be utilized in practice and affect those who become pathologized. Drawing on research findings in the field of HIV/AIDS nursing, we portray the impact of psychiatric pathologization of women who see their bodies transformed by antiretroviral therapy and subsequently, must accept that their response to these changes become the target of psychiatric labelling and interventions. In this paper, we therefore engage with the reader in a critical analysis that exposes the tensions that exist between individual experiences of distress and psychiatric pathologization. The first section of the paper is dedicated to the presentation of a theoretical framework in which we explore the medicalization process and the ontological issues regarding psychiatric diagnoses. The second section seeks to present the results from a qualitative research study in the field of HIV/AIDS nursing and serves as a case example to foster discussions on the implications of psychiatric pathologization in clinical practice.
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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 | 0.060 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".