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Record W2490778267 · doi:10.1177/2055102916650094

Managing the stigma: Exploring body image experiences and self-presentation among people with spinal cord injury

2016· article· en· W2490778267 on OpenAlexaff
K. Alysse Bailey, Kimberley L. Gammage, Cathy van Ingen, David S. Ditor

Bibliographic record

VenueHealth Psychology Open · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsBrock University
Fundersnot available
KeywordsSpinal cord injuryConstructivist grounded theoryDisconnectionGrounded theoryPresentation (obstetrics)PsychologyStigma (botany)Physical therapyMedicinePhysical medicine and rehabilitationClinical psychologySpinal cordQualitative researchSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Using modified constructivist grounded theory, the purpose of this study was to explore body image experiences in people with spinal cord injury. Nine participants (five women, four men) varying in age (21-63 years), type of injury (C3-T7; complete and incomplete), and years post-injury (4-36 years) took part in semi-structured in-depth interviews. The following main categories were found: appearance, weight concerns, negative functional features, impact of others, body disconnection, hygiene and incontinence, and self-presentation. Findings have implications for the health and well-being of those living with a spinal cord injury.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.002
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 head0.091
GPT teacher head0.468
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations42
Published2016
Admission routes1
Has abstractyes

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