An Exploration of Justice Personnel's Perceptions of and Experiences with Mentally Ill Offenders
Bibliographic record
Abstract
Deinstitutionalization and increased criminalization of mentally ill offenders result in their overrepresentation within the criminal justice system. Justice personnel now encounter mentally ill offenders more frequently. Yet, their workplace experiences with and perceptions of mentally ill offenders are largely misunderstood. Six semi-structured interviews with justice personnel from Western Canada were conducted. Inductive coding generated three preliminary themes. “It’s all about funding,” centers on staff shortages, inadequate prison services, fragmented community supports, and a lack of affordable housing. “If it bleeds, it leads” identifies negative media framing that contributes to mental illness stigma and police demonization, which increases public fear and disdain of mentally ill offenders more broadly. Finally, “They’re just people,” suggests the perceptions and experiences of justice personnel are predominantly positive. These findings shed light on the lived realities of justice personnel and are an integral first step to informing policy, improving service delivery, and identifying programming needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".