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Record W2006256439 · doi:10.1136/ebn.11.3.94

Women with spinal cord injuries underwent a process of discomfort, moving towards comfort, and comfort in dealing with their changed bodiesCommentary

2008· letter· en· W2006256439 on OpenAlexaffabout
Stacey Lessard

Bibliographic record

VenueEvidence-Based Nursing · 2008
Typeletter
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSpinal cordProcess (computing)MedicineSpinal cord injuryPhysical medicine and rehabilitationPsychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

K Yoshida Dr K Yoshida, University of Toronto, Toronto, Ontario, Canada; karen.yoshida@utoronto.ca How do women with spinal cord injuries (SCIs) feel about living with their changed bodies? Secondary analysis of semi-structured interviews using a modified grounded theory method. 6 cities in Ontario, Canada. 15 women, 18–60 years of age, who had incomplete or complete quadriplegia or paraplegia. Women were interviewed for 2 hours on their perceptions of rehabilitation and recommendations for improving the process. Interviews were transcribed verbatim and coded; 20 codes related to 2 themes (self/self-image/self-concept and change with SCI) were extracted for secondary analysis using memoing, integration of codes, and concept development. A 3-phase process reflected the experiences of women living with physical body changes due to SCIs. (1) Discomfort . Initially, women felt “ill at ease” with body changes and struggled to integrate their new body image with their sense of self. Early stages of rehabilitation were associated with generally negative emotions about self, particularly because of the challenge of accepting a new physical appearance and lifestyle adjustments. Loss …

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)0.001
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.066
GPT teacher head0.362
Teacher spread0.296 · 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
GenreCommentary

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

Citations0
Published2008
Admission routes2
Has abstractyes

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