Bibliographic record
Abstract
Abstract Social inclusion is an explicit goal of legislation, policies, and supports for persons with intellectual and developmental disabilities in many countries. However, evidence outlining the dimensions of social inclusion is still limited. How we understand social inclusion defines how it is measured. This study aims to better understand the concept and indicators of social inclusion. Retrospective analyses were conducted on 1,341 adults with intellectual disabilities residing in institutional and community‐based settings who were assessed with the “interRAI Intellectual Disability” instrument. Objective and subjective items in the instrument related to five domains of social inclusion (i.e., relationships, leisure, productive activities, accommodations, and informal support). The results highlighted the heterogeneity within domains, and by the nature of the indicator. Overall, percentages varied between 3.0% and 96.4% depending on which indicator was used; variability also existed in rates achieved using objective and subjective indicators. Acceptable‐to‐good levels of internal consistency were reported for three of the five domains; low correlations were found to exist between some, but not all, domains. The results of this study demonstrate that without an understanding of what social inclusion means for both general and vulnerable populations, it is not clear what is being measured, or how it should be measured. A clear definition of inclusion and its measurement is needed for decision‐makers and service providers to define the nature of their responsibilities, set actions, and assess their effectiveness in achieving inclusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".