The medicalisation of the dying self: The search for life extension in advanced cancer
Bibliographic record
Abstract
Although many studies have previously examined medicalisation, we add a new dimension to the concept as we explore how contemporary oncological medicine shapes the dying self as predominantly medical. Through an analysis of multiple case studies collected within a comprehensive cancer centre in Ontario, Canada, we examine how people with late-stage cancer and their healthcare providers enacted the process of medicalisation through engaging in the search for oncological treatments, such as experimental drug trials, despite the incurability of their disease. The seven cases included 20 interviews with patients, family, physicians and nurses, the analysis of 30 documents and 5 hr of field observation. A poststructural perspective informed our study. We propose that searching for life extension enacts medicalisation by shaping the dying person afflicted with terminal cancer into new medical subjectivities that are knowledgeable, active, entrepreneurial and curative. Participants initially took up medical thinking from the formal oncology system, but then began to apply and internalise medical rationalities to alter their personhood, thereby generating new curative possibilities for themselves. For people seeking life extension, the embodied and day-to-day experiences of suffering and being close to death became expressed and moderated in fundamentally medicalised terms.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".