Age, Gender, and Treatment Attendance Among Forensic Psychiatric Outpatients
Bibliographic record
Abstract
The records of 1,185 female and 5,114 male forensic psychiatry outpatients, within the age range of 15-44 years, were used to evaluate absenteeism from treatment in relation to age and gender. Age was divided into the 3 age groups 15-24, 25-34, and 35-44. Females had a significantly higher absentee rate than males in all age groups (p < .05). This gender difference is likely due to varying social roles as well as differential perceptions of treatment relevance. For both males and females, missed appointments declined significantly with age (p < .05). This age effect likely results from the decline in risk-taking and impulsivity with age. Because the efficacy of any treatment lies in a client's continued participation, it is important to address program attendance barriers and commitment to treatment issues. Commitment to treatment can be increased by active follow-up on non-attendance and by providing a positive therapy experience.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".