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Record W2004167730 · doi:10.1080/09638280902736346

Factors affecting measures of activities and participation in persons with mobility impairment

2009· article· en· W2004167730 on OpenAlexafffund
Joy Wee, Rosemary Lysaght

Bibliographic record

VenueDisability and Rehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsQueen's University
FundersOntario Medical Association
KeywordsActivities of daily livingPsychologyScale (ratio)Affect (linguistics)GerontologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: This study examined important factors affecting activities and participation of persons with mobility impairments. METHOD: This mixed methods study employed grounded theory approaches and data conversion to quantify impact of environmental and personal factors on standard measures of activities and participation. Semi-structured interviews of participants with mobility impairments were conducted to identify key factors and the magnitude of their influence on activities of daily living and participation. Participants were individually scored on the Barthel index (BI) and the Participation scale (P-scale); BI scores that would have been obtained without associated factors were estimated. Average cumulative impact of factors on BI and P-scale scores were estimated to identify factors with the greatest numeric impact. RESULTS: Twenty-four participants identified 258 factors that impacted activities. For the BI, adaptive equipment, gait aids, wheelchairs, scooters and home modifications were most influential. For the P-scale, personality, community and home accessibility, level of impairment, mobility aids and transportation were among the most influential. CONCLUSION: Convergent results through various methods suggest that reporting of contextual factors that may affect scores on standardised measures of activities and participation may assist in interpretation, and identification of interventional needs at the individual or system levels.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.302
Teacher spread0.278 · 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.

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

Citations53
Published2009
Admission routes2
Has abstractyes

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