The Social Networks of People with Intellectual Disability Living in the Community 12 Years after Resettlement from Long‐Stay Hospitals
Bibliographic record
Abstract
Background The social inclusion of people with intellectual disabilities presents a major challenge to services. As part of a 12‐year follow up of people resettled from long‐stay hospitals, the size of 213 individuals’ social networks and the types of social support they received were investigated, as viewed by people with intellectual disabilities themselves. The types of support received in four different kinds of community accommodation were compared. Method Individuals were interviewed and their social support networks mapped using a Social Network Guide developed for the study. Descriptive statistics were generated and comparisons made using generalized linear modelling. Results The sample comprised 117 men (average age 51 years) and 96 women (average age 56 years). All but seven were White British, 92% were single and they had in general, mild to moderate intellectual disabilities. The average network size was 22 members (range 3–51). The mean density was 0.5. A quarter of all network members were other service users with intellectual disabilities and a further 43% were staff. Only a third of the members were unrelated to learning disability services. In general, the main providers of both emotional and practical support were staff, although these relationships were less likely to be described as reciprocal. Other people with intellectual disabilities were the second most frequent providers of all types of support. People in small group homes, hostels and supported accommodation were significantly more likely to report close and companiable relationships than those in residential and nursing homes, but they also reported a greater proportion of critical behaviour. Conclusions The social networks revealed in this study are considerably larger than those of previous studies which have relied on staff reports, but findings about the generally limited social integration of people with intellectual disabilities are similar. A clearer policy and practice focus on the desirability of a range of different social contexts from which to derive potentially supportive network members is required so that people do not remain segregated in one area of life.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".