The Centre for Addiction and Mental Health Concurrent Disorders Screener
Bibliographic record
Abstract
OBJECTIVES: To review the characteristics of psychiatric screening tools currently available in addiction treatment services for rapid assessment of comorbid pathology and to introduce the Centre for Addictions and Mental Health Concurrent Disorders Screener (CAMH-CDS), a computer-administered questionnaire that screens for the occurrence of 11 Axis I disorders plus all substance use disorders, as well as for a history of conduct disorder. METHODS: We describe the structure, contents, and application of the CAMH-CDS. We undertook a sensitivity and specificity trial involving 171 subjects, a test-retest reliability study with 301 participants, and an open-label concordance study with 656 respondents. All subjects were regular clients of a major addiction treatment facility. RESULTS: The CAMH-CDS was easily and effectively used by addiction counsellors with limited or no mental health training. It has a low rate of false-negative responses, and it yields excellent test-retest reliability figures. It is highly sensitive to identifying persons with psychiatric disturbances; however, its ability to discriminate among specific disorders appears to be more limited. CONCLUSIONS: The CAMH-CDS can be reliably used to rule out the presence of psychiatric comorbidity in addiction service populations. As with other psychiatric screening instruments, its sensitivity values are stronger than its specificity values. The use of nonstructured clinical evaluations as the gold standard for diagnosis and a likely variance in the patients' symptom reports between the 2 examinations may have contributed to the latter finding.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".