“My Values Keep Me Well… Would They Help Other People?”: A Thematic Analysis Exploring the Values and Values-Based Behaviours of People Facing Severe and Multiple Disadvantage
Bibliographic record
Abstract
Individuals facing Severe and Multiple Disadvantage (SMD) have experience of at least three of the following: homelessness, substance use, mental illness, offending, and domestic violence. There is a push towards providing better support for people facing SMD and yet little research on what the “best” support looks like. Values motivate behaviour across various contexts, and helping an individual identify their values can lead to greater enactment of positive behavioural change. This study aimed to identify and explore the values of people facing SMD, the barriers and facilitators to enacting these values, and the perceived helpfulness of service provision in encouraging values-based behaviour or change. Twelve participants took part in semi-structured interviews. Reflexive thematic analysis resulted in four themes: values are idiosyncratic and interconnected; the benefits of value identification and enactment; the risks and challenges of value identification and enactment; and the relationship between values and support. Results offer preliminary evidence for the potential use of values in providing helpful, person-centered support for people facing SMD. Values work could arguably be integrated into any level of support for those facing SMD to support values-based living and change. Further research on the use and efficacy of values-focused interventions in SMD is needed.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".