A posthuman decentring of person-centred care
Bibliographic record
Abstract
In this paper, we examine person-centred care through a Deleuzian posthuman lens with the aim of exploring what becomes possible when the concepts of both person and care are de-centred. We do so through a consideration of the sets of relations that produce ‘the client’ in health care contexts. Our analysis maps particular entangled material-semiotic forces producing ‘M/michael’, a young man with a diagnosis of Duchenne muscular dystrophy, within a rehabilitation clinic. Drawing on Deleuzian notions of assemblage, affect, and becoming we explore ‘person-care’ as an active production that dynamically enacts persons-as-clients through clinical arrangements. Persons are thus reconceptualised in terms of locally produced subject positions and their care relations, rather than pre-existing beings who can be ‘centred’ within health services. Paradoxically, by de-centring persons and care, we work to conjure ways to strengthen the aspirations of person centredness to humanise health practices. In doing so, we consider different possibilities for re-imagining clinical work and contribute to debates regarding how healthcare conceptualises and addresses disability, health, and wellbeing. We suggest that such posthuman analyses can open up new ways of understanding and re/forming healthcare.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.086 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".