Accounting for Cognitive Impairment in Concurrent Disorders Treatment: Practical Resources to Meet the Needs of Our Most Complex Clients
Bibliographic record
Abstract
Much has been written about the interplay between mental health and substance use disorders. However, there is a third dimension, which often complicates the provision of care and renders many mainstream approaches less effective. Cognitive impairments, including those that arise from traumatic brain injury, are increasingly being recognized as an important consideration in addictions programming. This article will discuss the findings from a cross-sectorial partnership between the Centre for Addiction and Mental Health (CAMH) and Community Head Injury Resource Services of Toronto (CHIRS). Four recommendations for program administrators are proposed: (1) support screening for brain injury; (2) use free resources to train staff members to recognize, accommodate and address neurocognitive impairment; (3) establish cross-sector partnerships to facilitate collaborative programming and cross training for the most complex clients being served; (4) when developing new programming, include structures and behavioural interventions that have been shown to benefit individuals with cognitive impairment.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".