Medicating the Crisis: Investigating the Links Between Precarious Employment, Mental Health Issues, and the Reliance on Antidepressants as Treatment
Bibliographic record
Abstract
My research engages in literature that discusses the relationship between precarious employment conditions, declines in mental health, and the way mental health issues are treated with pharmaceutical technologies – namely antidepressant medication. In this dissertation, I aim to contribute to this discussion by a) situating the relationship between precarious labour conditions and mental health within a specifically capitalist society, and b) investigating the pharmaceutical treatment of individual illness experiences and mental health issues that can be linked back to broader social structures. In doing so I provide an analysis of the process of medicalization, which locates mental health aetiology as primarily biochemical and focuses on medical, commodity-based interventions, specifically antidepressants, in response to illness experiences – where dis-ease becomes disease. My primary method is a theoretical analysis of capitalist commodity production, the social process of medical knowledge production, medicalization, and development of pharmaceutical technologies. The goal of my theoretical analysis is to achieve new insight regarding my research questions by bringing together existing bodies of literature. A narrative analysis of stories gathered through in-depth interviews and autoethnographic accounts complement the main theoretical analysis, and are used to explore personal experiences of precarious social conditions related to mental health and work. I pay particular attention to systems of oppression enabled and fed through capitalism, such as gender, ability, and class relations. I argue that a reliance on antidepressant medication in response to the distress of working people and the unemployed poor plays an important role in enabling the continuation of dysfunctional social conditions. I argue that classifying mental health issues as purely medical erases social structural factors in the development of illness, and removes the serious consideration of such factors from diagnosis and treatment. This erasure also limits people’s capacity to act on pertinent questions they may have regarding their own emotional fulfillment and social wellbeing. Such disempowerment frustrates the radical imagination and pursuit of more sustainable and equitable ways of developing and maintaining genuine health.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".