Temporal trends in rates of dual diagnoses at a Canadian addictions hospital over a five-year period
Bibliographic record
Abstract
OBJECTIVES: Dual diagnosis refers to co-occurring substance use and psychiatric disorders. The principal aims of this investigation were two-fold: 1) to identify aspects of patients' drug use and prior treatment histories associated with their receiving a dual diagnosis upon admission to the Donwood Institute, a residential drug treatment facility located in Toronto, Canada; 2) to track temporal trends in the rates of diagnosed comorbidities over a five-year period at this same institution. METHODS: We conducted an analysis of the intake assessment forms and hospital records of 159 patients who had been admitted to a drug treatment facility during the month of September for each of the years between 1998 and 2002 inclusive. Comparisons were made between patients who had received a psychiatric diagnosis on admission and patients who had received no such diagnosis. We then employed logistic regression analyses to explore the relationship of the variable psychiatric diagnosis on admission to other patient variables. RESULTS: Among the patients studied in our sample, those receiving psychotherapy or taking prescription psychotropic medication at the time of their admission as well as patients whose primary problem substance was cannabis or who had been previously admitted to the treatment facility were significantly more likely to have received a psychiatric diagnosis on admission, in spite of our finding that several patients receiving psychotherapy or taking at least one psychotropic medication did not receive a psychiatric diagnosis on admission. CONCLUSIONS: Whilst our data indicate that psychiatric comorbidity is common among individuals in treatment for substance use disorders at the Donwood Institute, it is possible that some individuals with psychiatric illness in our sample were not diagnosed as such when presenting for treatment of their substance use difficulties. Moreover, temporal tracking of rates of dual diagnoses did not reveal a consistent increase during the period studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".