The ‘disabilitization’ of medicine: The emergence of Quality of Life as a space to interrogate the concept of the medical model
Bibliographic record
Abstract
This article presents an archaeological inquiry into the early histories of Quality of Life (QoL) measures, and takes this as an occasion to rethink the concept of the ‘medical model of disability’. Focusing on three instruments that set the ground for the emergence of QoL measures, namely, the Karnofsky Performance Scale (KPS, 1948), and the classification of functional capacity as a diagnostic criterion for heart diseases (Bainton, 1928) and as a supplementary aid to therapeutic criteria in rheumatoid arthritis ( Steinbrocker, Traeger, and Batterman, 1949 ) – I discuss how medicine, throughout the emergence of QoL, began to expand its gaze beyond the confines of the body to what that body does in daily life. Building upon Armstrong et al.’s notion of ‘distal symptoms’ (2007) and Wahlberg’s idea of ‘knowledge of living’ (2018), I propose the notion of disabilitization to encapsulate this expansion of the clinical gaze, through which medicine has come to articulate diseases and their treatments in new ways, and in so doing, has inadvertently created disability as a new kind of knowledge category in itself – a category that is defined not through its reduction to mere pathology, but through its dispersal into everyday life. I present this concept not as a periodization, but as a provocative discontinuity with the totalizing history assumed within the medical model of disability, and in so doing, ask what, in fact, holds ‘the medical model’ together, and whether there might be other ways of understanding medicine’s complex relationship to disability than what the concept of the medical model allows us to envisage.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.147 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| 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 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".