Improving Quality of Life Outcomes in Supported Accommodation for People with Intellectual Disability: What Makes a Difference?
Bibliographic record
Abstract
BACKGROUND: The quality of life (QOL) of people with intellectual disability living in supported accommodation services is variable, influenced by many possible factors. Various frameworks have attempted to identify these factors without assigning value, direction of influence or relative impact on outcomes. METHODS: A realist review of the literature aimed to expose different propositions about variables influencing QOL outcomes and review the strength of supporting evidence for these, to identify their relative influence. Evidence was reviewed for and against each of five clusters. RESULTS: Evidence was strongest for the presence of staff practices (use of Active Support), front-line management practice (use of practice leadership), culture (enabling and motivating), human resources policies and practice (that support front-line leaders and recruitment of staff with the right values), adequate resources, and small, dispersed and homelike settings. CONCLUSIONS: The evidence informs policy and practice but in some clusters remains limited, warranting further research which measures outcomes on all QOL domains.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".