Predicting Educational Attainment Based on Forensic Psychiatric Patients' Age at First Hospitalization.
Bibliographic record
Abstract
Education during recovery could impact a forensic psychiatric patient's community reintegration; however, individual education goals for patients can be difficult due to the lack of available parameters. The purpose of this study was to test whether age at first hospitalization is predictive of educational attainment among forensic psychiatric patients and to determine which ages of first hospitalization best predict 8 levels of educational attainment. Cattell's intelligence theory served as the theoretical framework for this study because mental illness requiring early hospitalization may affect education and learning. This quantitative, nonexperimental study involved a predictive design with data from the Canadian Institute for Health Information database. The sample of patients from 2011-2016 consisted of 16,639 diagnosed with schizophrenia or other psychotic disorder and 2,227 diagnosed with mood disorder. Multinomial logistic regression analysis indicated age at first hospitalization to be a predictor of educational attainment among both categories of diagnoses. Odds ratio analyses identified which ages of first hospitalization best predict 8 levels of educational attainment. Increased rates of education levels were indicated when age at first hospitalization increased. Patients were more likely to attain a high school diploma than drop out between 9th to 11th grade unless first hospitalized at age 14 or under. Based on the results from this study, completion of a general equivalency diploma or a life skills program may provide additional opportunities for independent living and employment, which can improve the lives of patients and those in the community. Therefore, this project can lead to social change by encouraging changes through the results and recommendations presented in a white paper.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".