Exploring Social Interactions in the Context of Justice System Involvement: Perspectives of Patients and Psychiatric Nurses
Bibliographic record
Abstract
Psychiatric nurses who work with people who are involved with the justice system experience ethical and moral tension arising from their dual role (care and control). This is known to significantly affect the development of a therapeutic relationship between nurses and patients. (a) better understand how justice system involvement affects people living with mental disorders and the nurses who work with them; (b) explore the influence of judiciarization on social interactions between these actors. Grounded theory (GT) was used as the qualitative methodology for this research. Semi-structured interviews were conducted with participants. The study was carried out in three different units of a psychiatric institution: Psychiatric Intensive Care Unit, Emergency Department, and Brief Intervention Unit. A sample of 10 patients and 9 psychiatric nurses was recruited ( n = 19). Theoretical sampling was used to recruit participants. We followed the iterative steps of qualitative GT analysis (open coding, axial coding, constant comparison, and modelization). Three main themes emerged from the qualitative analysis: (a) Experience of Justice System Involvement, (b) Crisis, (c) Relational Aspects and Importance of the Approach. These results will inform nurses and healthcare providers about the impacts of justice system involvement on people living with mental illness and how clinical practices can be better adapted to this population with complex health needs.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| 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".