The Judiciarization of People Living with Mental Illness: A Grounded Theory on the Perceptions of Persons Involuntary Admitted in Psychiatric Institution
Bibliographic record
Abstract
The involvement of people living with mental illness in the judicial process, whether in civil or criminal justice system, is a growing phenomenon that can be defined as judiciarization.Such over-representation of people with mental illness in the justice system is related to several issues, including stigma, experienced coercion, loss of autonomy and social isolation.To explore this understudied phenomenon in nursing research, we conducted a study to better understand how judiciarization affects people living with mental illness.The specific objectives were: 1) to understand how insertion into a judicial process affects people living with mental illness; 2) to explore the perception of these people and their lived experience within the judicial trajectory.For the methodology, grounded theory was used as a research model.The theoretical framework of the total institution, proposed by Erwin Goffman, was used conceptually.Participants were recruited from a university-affiliated hospital.Hospitalized persons who had been involved in the justice system were interviewed (n = 10).Three conceptualizing categories were identified through the analyzed data: 1) Diversity of Judicial Trajectories; 2) Involuntary Psychiatric Admission Process; 3) Judiciarization Lived as a Complex Experience.The results of this research can be used to better inform nurses, clinicians, and policy makers about the impacts of the judiciarization of mental illness, and how clinical practices can be better adapted to populations with very 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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".