Time of Day Matters: An Exploratory Assessment of Chronotype in a Forensic Psychiatric Hospital
Bibliographic record
Abstract
A growing body of evidence links the late chronotype to mental illness, aggression, and aversive personality traits. However, much of what we know about these associations is based on healthy cohorts, and it is unclear how individuals with high levels of aggression, including forensic psychiatric populations, but not offenders, are affected. The present study aimed to measure chronotype in a forensic psychiatric inpatient population, evaluate the impact of diagnosis, and identify any interactive relationships between chronotype, diagnosis, aggression, and dark triad traits. Subjects completed the reduced Morningness-Eveningness Questionnaire (rMEQ), Munich ChronoType Questionnaire (MCTQ), Pittsburgh Sleep Quality Index (PSQI), Buss Perry Aggression Questionnaire-Short Form (BPAQ-SF), and Short Dark Triad Questionnaire (SD3). We sampled 55 forensic psychiatric patients (52 males) between the ages of 23 and 73 years (mean ± SD: 39.6 ± 14.3 years). Among the patients sampled, 25% were evening types and 36% were morning types. Eveningness was greater in patients with a personality disorder; however, no chronotype differences were found for psychosis patients. Patients without psychosis had a positive association between anger and eveningness, as well as between hostility and eveningness. For subjects with a substance use disorder, morningness was positively associated with narcissism. Conversely, an association between eveningness and greater narcissism was identified in patients who did not have a substance use disorder. These findings suggest that, compared to the general population, evening types are more prevalent in forensic psychiatric populations, with the strongest preference among patients diagnosed with a personality disorder. No differences in chronotype were identified for psychosis patients, which may be related to anti-psychotic medication dosing. Given the sex distribution of the sample, these findings may be more relevant to male populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".