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Record W2463066196

Personal and environmental barriers and facilitators to social participation among Canadian adults with mobility disabilities

2014· dissertation· en· W2463066196 on OpenAlexaboutno aff
Stephanie Beveridge

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2014
Typedissertation
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsLogistic regressionPsychologyDescriptive statisticsMultivariate analysisQuality of life (healthcare)Social engagementGerontologyUnivariatePopulationPersonal developmentEnvironmental healthMedicineMultivariate statisticsSociologySocial science
DOInot available

Abstract

fetched live from OpenAlex

The prevalence of mobility disabilities is increasing in part due to the ageing population. People with mobility disabilities are at an increased risk of reduced social participation and activity limitations and thus reduced well-being. Social participation is important to one’s health and quality of life. The purpose of this secondary data analysis study was to explore factors (personal and environmental) that were most influential to social participation levels among adults aged 20-64 (N = 6105). Statistics Canada’s 2006 Participation and Activity Limitation Survey was used. Data analysis included descriptives of sociodemographics, personal and environmental barriers and facilitators and logistic multinominal univariate and multivariate regression. There was no clear trend as to whether personal or environmental factors were the strongest predictors to social participation. The results of this study suggest a complex interaction between personal and environmental factors that constrain and promote social participation; it provides the foundation for further empirical research to increase activity participation and mobility.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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