Review Paper Cost-Effectiveness of Cognitive-Behavioural Therapy for Mental Disorders: Implications for Public Health Care Funding Policy in Canada
Bibliographic record
Abstract
The total cost of mental illness in Canada has been esti-mated at $14.4 billion (1998 dollars), which includes both direct and indirect costs (1). Mental illness accounts for more than 50 % of physician billing and uses more hospital bed-days than cancer (2). Medications are considered first-line, mainstay treatment for mental health disorders and contribute most to rising care costs (3). There is growing interest, therefore, in nonpharmacologic interventions with strong empirical sup-port, such as CBT. Can J Psychiatry, Vol 51, No 10, September 2006662 Objective: Publicly funded cognitive-behavioural therapy (CBT) for mental disorders is scarce in Canada, despite proven efficacy and guidelines recommending its use. This paper reviews published data on the economic impact of CBT to inform recommendations for current Canadian mental health care funding policy. Method: We searched the literature for economic analyses of CBT in the treatment of mental disorders. Results: We identified 22 health economic studies involving CBT for mood, anxiety, psychotic, and somatoform disorders. Across health care settings and patient populations, CBT alone or in combination with pharmacotherapy represented acceptable value for health dollars spent, with CBT costs offset by reduced health care use. Conclusions: International evidence suggests CBT is cost-effective. Greater access to CBT would likely improve outcomes and result in cost savings. Future research is warranted to evaluate the economic impact
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".