Understanding the social inclusion needs of people living in mental health supported accommodation
Bibliographic record
Abstract
Abstract Objectives To identify the social inclusion needs that were i) most commonly identified and ii) most and least commonly prioritised as support planning goals for mental health service users living in supported accommodation, using the online Social Inclusion Questionnaire User Experience (SInQUE). We qualitatively examined mental health supported accommodation staff and servicer users’ views on barriers to offering support with two less commonly prioritised areas: help finding a partner and feeling less lonely. Methods Anonymous SInQUE data were collected during a completed study in which we developed and tested the online SInQUE. Four focus groups were conducted with mental health supported accommodation staff (N=2) and service users (N=2). Results The most common social inclusion needs identified by service users (n=31) were leisure activities, finding transport options, and feeling less lonely. Of the needs identified, those that service users and staff least frequently prioritised as support planning goals were having company at mealtimes, getting one’s own furniture, feeling less lonely, help with finances, and help finding a partner. In the focus groups, staff and service users identified barriers to helping with loneliness and finding a partner which related to staff and service users themselves, supported accommodation services, and wider societal factors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 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".