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Record W4367675465 · doi:10.1136/medhum-2022-012484

Cripping the pain scale: literary and biomedical narratives of pain assessment

2023· article· en· W4367675465 on OpenAlexfundno aff
Neko Mellor

Bibliographic record

VenueMedical Humanities · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersHumanities Research Group, University of Windsor
KeywordsNarrativeEmbodied cognitionPerformative utteranceChronic painReading (process)PsychologyCognitive psychologyLiteratureAestheticsArtLinguisticsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Establishing first a brief history of methods attempting to quantify pain before my close reading, I read both Biss' and Huber's accounts as performative explorations of the limitations of using linear pain scales for pain which is recursive and enduring. Considering both texts as cripistemologies of chronic pain, my literary analysis attends to their criticism of the pain scale, including its implicit reliance on imagination and memory, and how its unidimensionality and synchronic focus prove inadequate for lasting pain. For Biss, this surfaces as a quiet critique of numbers and a disturbance of their fixity, while Huber's criticism employs the motif of pain's legibility across multiple bodies to spell out alternative meanings of chronic pain.Crucially, this article proposes a crip and embodied approach for reading and responding to accounts of chronic pain's measurement, including Biss' and Huber's literary accounts, and the biomedical account of pains scales which this article reads alongside them. The article's analysis draws on my personal experience of chronic pain, neurodivergence and disability to demonstrate the generativity of an embodied approach to literary analysis. Rather than bowing to the impulse to impose false coherence on my reading of Biss and Huber, my article foregrounds the impact of the re-reading, misreading, cognitive dissonance and breaks necessitated by chronic pain and processing delays on this analysis. In bringing an ostensibly crip methodology to bear on readings of chronic pain, I hope to invigorate discussions on reading, writing and knowing chronic pain in the critical medical humanities.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.336
Teacher spread0.301 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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