Correctional Officers' Perceptions of Inmates with Mental Illness: The Role of Training and Burnout Syndrome
Bibliographic record
Abstract
This study was designed to examine correctional officers' (COs') perceptions of offenders with mental disorders (MDOs) in relation to non-disordered offenders, patients with mental illness, and the general public as a replication of research conducted by Kropp, Cox, Roesch, and Eaves (1989). The objective was to investigate i) the nature of COs' perceptions and how they have changed over time, ii) the type of training officers receive to manage MDOs, and iii) the impact of training and burnout on perceptions towards the four target groups. Results indicated that while perceptions toward MDOs have become increasingly positive over time, this group continues to be viewed unfavorably relative to other non-incarcerated populations. Compared to non-disordered offenders, officers held more positive attitudes toward MDOs. Training specifically related to mental illness was associated with more positive perceptions of MDOs. Emotional exhaustion predicted poorer perceptions of MDOs. Depersonalization predicted poorer perceptions of non-disordered offenders. Officers advocated the need for supplementary training to effectively manage MDOs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".