MétaCan
Menu
Back to cohort
Record W2107364128 · doi:10.1177/1049732311417726

Getting on With Life

2011· article· en· W2107364128 on OpenAlexafffund
Eleanor Weitzner, Susan Surca, Sarah Wiese, Andrea Dion, Zoe Roussos, Rebecca Renwick, Karen Yoshida

Bibliographic record

VenueQualitative Health Research · 2011
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of Toronto
FundersOntario Neurotrauma Foundation
KeywordsPsychologyQualitative researchMedical model of disabilityInternational Classification of Functioning, Disability and HealthSpiritualityClinical psychologyPersonalityDevelopmental psychologySocial psychologyMedicineRehabilitationPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Currently, the dominant cultural beliefs toward disability are negative, and the existing literature is limited with respect to examining how people are using and/or viewing their disabilities positively. The purpose of this study was to identify how individuals living with a spinal cord injury (SCI) viewed and/or used their disability positively, and what contextual influences facilitated this positive approach. This study was a secondary analysis of qualitative data from a larger study. The findings revealed three levels at which disability was viewed and/or used positively by people with SCI: self, peers, and disability community. In addition, several aspects of the participants' situations were found to facilitate this positive view and/or use of disability: personality, spirituality, support systems, and acceptance of one's disability. The findings reveal that individuals with SCI are viewing and/or using their disabilities positively in many different ways. This study has significant implications for the direction of future research and for health care professionals who need to increase their advocacy and facilitating roles.

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)0.019
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.005

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 head0.842
GPT teacher head0.669
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.

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

Citations53
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicFamily and Disability Support ResearchFrench-language works237,207