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Social inclusion of people with ID from different cultural backgrounds.

2010· article· en· W2254336316 on OpenAlexaboutno aff
A. Bhwardwaj, Rachel Forrester‐Jones, Glynis H. Murphy

Bibliographic record

VenueStrathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2010
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationOperationalizationInclusion (mineral)PsychologySocial engagementPopulationSample (material)Social integrationSocial psychologyDevelopmental psychologyGerontologySociologyMedicineDemographySocial science

Abstract

fetched live from OpenAlex

Aim: To examine social inclusion among adults with ID and to determine the extent to which it differed depending on the de?nition and measurements used. 
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\nMethod: Social inclusion was measured using the following items in the interRAI ID instrument: (a) social relationships (presence of a con?dant, recent contact with family/friends), (b) participation in social activities of interest, and (c) involvement in structured activities (work, volunteer services, day programmes). Population-level data in Ontario’s institutions (1014 people assessed in 2005) and a sample of 327 community-dwelling adults (collected between 2005 and 2007) were used. 
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\nResults: Social inclusion differed between persons living in institutional and community settings and within each group based on the conceptualization used. Further, there was great variability within and between groups based on the speci?c measures used within each conceptualization. For example, the rates differed greatly for the three measures of social relationships and the three measures of involvement in structured activities.
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\nConclusion: The ?ndings replicated those of other studies showing greater social inclusion among persons with ID living in community settings. The results also showed that social inclusion differed based on how the concept was de?ned, and what measurements were used to operationalize that de?nition. Findings highlight the need for a common framework for understanding and measuring social inclusion.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.004
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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 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
Published2010
Admission routes1
Has abstractyes

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