Development and preliminary testing of an online tool to assess social inclusion and support care planning in mental health supported accommodation
Bibliographic record
Abstract
Abstract Background Individuals with severe mental illness living in supported accommodation are often socially excluded. Enabling social inclusion is an important aspect of recovery-based practice, and improves quality of life. The Social Inclusion Questionnaire User Experience (SInQUE) is a measure of social inclusion that has been validated for use with people who have mental health problems. Previous research has suggested that the SInQUE could also help to support care planning focused on enabling social inclusion in routine mental health practice. Objectives To develop an online version of the SInQUE for use in mental health supported accommodation services, and examine its acceptability and perceived usefulness as a tool to support care planning with service users. Methods i) A lab-testing stage to assess the acceptability of the SInQUE tool through ‘think-aloud’ testing with six supported accommodation staff; ii) A field-testing stage to assess the acceptability, utility, and use of the SInQUE tool over a 5-month period. An implementation strategy was employed in one London borough to encourage the use of the SInQUE. Qualitative interviews with 12 service users and 12 staff who used the tool were conducted and analysed using thematic analysis. Use of the SInQUE was compared with two other local authority areas, one urban and one rural, where the tool was made available for use but no implementation strategy was employed. Results In total, 17 staff used the SInQUE with 28 different service users during the implementation period (about 10% of all service users living in supported accommodation in the study area). Staff and service users we interviewed felt that the SInQUE was collaborative, comprehensive, and user-friendly. Staff deemed the tool relevant to their role. Although some staff were concerned that particular questions might be too personal, service users did not echo this view. Participants generally felt that the SInQUE could help to identify individuals’ priorities regarding different aspects of social inclusion through prompting in-depth conversations and tailoring specific support to address areas where service users would like to be more included. Some interviewees also suggested that the tool could highlight areas of unmet or unmeetable need across the borough, that could feed into service planning. The SInQUE was not used in the comparison areas that had no implementation strategy. Conclusions The online SInQUE is an acceptable and potentially useful tool, that can be recommended to assess and support care planning to enable social inclusion of people living in mental health supported accommodation services. Despite this, take-up rates were modest during the study period. A concerted implementation strategy is key to embedding its use in usual care, including proactive endorsement by senior leaders and service managers.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".