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Record W2020567551 · doi:10.3109/17483107.2010.522678

Electronic aids to daily living and quality of life for persons with tetraplegia

2010· article· en· W2020567551 on OpenAlexaff
Patricia Rigby, Stephen E. Ryan

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2010
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTetraplegiaQuality of life (healthcare)MedicineActivities of daily livingSpinal cord injuryPhysical therapyGerontologyPopulationCohortSpinal cordPsychiatryEnvironmental healthNursing

Abstract

fetched live from OpenAlex

PURPOSE: To compare the satisfaction with quality of life (QOL) of adults with tetraplegia from spinal cord injury (SCI) who use and do not use electronic aids to daily living (EADLs). METHOD: This study used a cross-sectional design. Thirty-six persons with spinal cord injuries or conditions at or above C5/6 level participated. Fifteen participants used EADL at home and 21 formed the comparison group of non-users of EADL; all were living in the community. We used the Quality of Life Profile-Physical Disabilities (QOLP-PD) to examine participant's QOL. RESULTS: Both groups rated the levels of importance of all aspects of QOL equally. The EADL users rated their satisfaction with QOL significantly higher for total QOLP-PD scores and for four of the nine domains, including all three domains of belonging. The groups did not differ in age, FIM scores, level of education, and hours of paid attendant care. The EADL user group had significantly more males than females, and had higher levels of SCI. CONCLUSIONS: EADLs appear to contribute to the experience of greater subjective QOL for persons with severe physical disability from high SCI. Prospective cohort studies designs that employ methods and analytic plans to study the causal effect of EADLs on QOL are recommended. The QOLP-PD was found to be a valid measure of QOL for this population.

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)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.365
Teacher spread0.343 · 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 designObservational
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

Citations38
Published2010
Admission routes1
Has abstractyes

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