The Clinical Profile and Service Needs of Hospitalized Adults With Mental Retardation and a Psychiatric Diagnosis
Bibliographic record
Abstract
OBJECTIVE: This study compared patients with both mental retardation and a psychiatric diagnosis with patients who did not have co-occurring mental retardation who were served in Ontario's tertiary mental health care system in terms of demographic characteristics, symptom profile, strengths and resources, and clinical service needs. METHODS: A secondary analysis of data from the Colorado Client Assessment Record (CCAR) that were collected between 1999 and 2003 from all tertiary psychiatric hospitals in Ontario, Canada, was completed for a random sample of 3,927 cases, representing 12,470 patients receiving psychiatric services. RESULTS: Patients with both mental retardation and a psychiatric diagnosis differed from those who did not have mental retardation in terms of demographic characteristics, diagnostic and symptom profile, resources, and recommended level of care. More specifically, patients with both mental retardation and a psychiatric diagnosis had significantly worse ratings across nearly all CCAR functional domains and were assessed as requiring more than the recommended levels of care compared with other patients. CONCLUSIONS: Patients who have both mental retardation and a psychiatric diagnosis constitute a sizeable subgroup of an already underserved psychiatric hospital population. Greater attention is required to meet the unique clinical and service needs of this challenging group.
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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 | 0.003 |
| 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) | 0.002 | 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".