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Record W3096311230 · doi:10.1177/1363459320969783

Enacting objects and subjects in a children’s rehabilitation clinic: Default and shifting ontological politics of muscular dystrophy care

2020· article· en· W3096311230 on OpenAlexaffabout
Patricia Thille, Thomas Abrams, Barbara E. Gibson

Bibliographic record

VenueHealth An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of TorontoQueen's UniversityUniversity of Manitoba
Fundersnot available
KeywordsMuscular dystrophyPoliticsRehabilitationMedicinePhysical medicine and rehabilitationPhysical therapySociologyPolitical scienceLawInternal medicine

Abstract

fetched live from OpenAlex

In health care clinics, problems are constructed through interactions, a choreography of human and non-human actors together enacting matters of concern. Studying the ways in which a body, person, family, or environment is objectified for clinical purposes opens discussion about advantages and disadvantages of different objectification practices, and exploration of creative ways to handle the diversity and tensions that exist. In this analysis, we explored objectifications in a Canadian neuromuscular clinic with young people with muscular dystrophy. This involved a close examination of clinical objectification practices across a series of 27 observed appointments. We identified the routinised clinical assessments, and argue these embed a default orientation to how to intervene in people's lives. In this setting, the routine focused on meeting demands of daily activities while protecting the at-risk-body, and working toward an abstract sense of an independent future for the person/body with muscular dystrophy. But the default could be disrupted; through our analysis of the routine and disruptions, we highlight how contesting visions for the present and future were consequential in ways that might be more than what is anticipated within rehabilitation practice.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.447
Teacher spread0.393 · 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.

Study designQualitative
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

Citations3
Published2020
Admission routes2
Has abstractyes

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