Using social bonding theory to examine ‘recovery’ in a forensic mental health hospital: A qualitative study
Bibliographic record
Abstract
BACKGROUND: For people living with mental illness, recovery involves learning to overcome and manage their symptoms and striving to live fulfilling lives. The literature on achieving recovery emphasises the importance of social connections and positive role models. Hirschi's social bonding theory posits that an individual's attachment to others, belief in social norms, and their commitment and involvement in conventional activities are the major contributors to normalising social behaviour. AIMS: The aim of this study is to understand the qualities of service identified by patients in a forensic hospital as being important and meaningful to recovery. METHODS: Semi-structured interviews with 30 inpatients in a forensic mental health hospital in British Columbia, Canada, were audio recorded, and the transcriptions were analysed using thematic analysis. RESULTS: Five themes emerged: involvement in programmes, belief in rules and social norms, attachment to supportive individuals, commitment to work-related activities and concern about indeterminacy of stay. CONCLUSIONS: The first four themes map closely onto Hirschi's criminologically derived social bonding theory; however, indeterminacy of stay also arose as a common theme. In addition, the theory was too simple in its separation of elements; our data suggested the complex integration of themes. Our findings may be useful for informing evaluation of forensic mental health services.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".