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Record W2966924563 · doi:10.1111/nin.12316

The medicalisation of the dying self: The search for life extension in advanced cancer

2019· article· en· W2966924563 on OpenAlexafffundabout
Shan Mohammed, Elizabeth Peter, Denise Gastaldo, Doris Howell

Bibliographic record

VenueNursing Inquiry · 2019
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkPublic Health OntarioUniversity of Toronto
FundersUniversity of Toronto
KeywordsPersonhoodEmbodied cognitionMedicalizationQualitative researchPsychologyMedicineSociologyPsychiatrySocial scienceEpistemologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.189
GPT teacher head0.475
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes3
Has abstractyes

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