Bibliographic record
Abstract
Weare pleased to note that persons with mental illness and developmental disability (that is, those with dual diagnoses) are identified as a tertiary care subpopulation by Cochrane and others (1).With respect to dual diagnosis, our current efforts at Queen's University and in southeastern Ontario include developing a model of community-based care and training those mental health professionals who have a special interest in this subpopulation.In the absence ofreliable data, we are assuming prevalence rates for mental retardation of 2% for children and 1% for adults.We are also assuming that 10% to 40% of this group have additional mental disorders.Our model ofcare assumes that a mental health team comprising a psychiatrist, a psychiatric social worker, a clinical psychologist, an occupational therapist, and a part-time speech language pathologist is available for every 100 000-150 000 persons.The mental health team oversees care in 3 "generic" services (for example, an emergency department, a mental health clinic, and an acute inpatient unit) and 3 "specialized" services (for example, a community behaviour-management program, a specialized assertive community treatment team, and an extended treatment unit).Community behaviour-management programs are already in place across Ontario; the other specialized programs are innovative but, in our view, totally defensible on the basis of experience in the Netherlands (2) and England (3).Attracting and training mental health professionals in the field of developmental disabilities is a continuing challenge.Once we have a complete model system established in the Kingston, Ontario, area, we propose to extend our existing training program in psychiatry (4)
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".