Alexithymia and Forgiveness Levels of Forensic Psychiatric Patients
Bibliographic record
Abstract
The current cross-sectional study was performed to examine levels of alexithymia and forgiveness in forensic psychiatric patients. Data were collected between March 2022 and August 2022 at a high-security forensic psychiatric hospital affiliated with a city hospital in Turkey. A personal information form prepared by the researchers, the Toronto Alexithymia Scale, and the Heartland Forgiveness Scale were used to obtain data. Participants comprised 132 forensic psychiatric patients who agreed to participate in the study. A significant negative correlation was found between alexithymia and forgiveness levels of participants ( p < 0.01, r = −0.259). In other words, as alexithymia levels increased, participants were found to be less forgiving. In addition, results suggest that forensic psychiatric patients are susceptible to alexithymia and higher levels of forgiveness. Determining forgiveness and alexithymia levels of forensic psychiatric patients will contribute to the structuring of care to be offered to these patients. [ Journal of Psychosocial Nursing and Mental Health Services, 62 (6), 27–35.]
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".