Cost, Effectiveness, and Cost-Effectiveness of a Collaborative Mental Health Care Program for People Receiving Short-Term Disability Benefits for Psychiatric Disorders
Bibliographic record
Abstract
OBJECTIVE: To examine the cost, effectiveness, and cost-effectiveness of a collaborative mental health care (CMHC) pilot program for people on short-term disability leave for psychiatric disorders. METHOD: Using a quasi-experimental design, the analyses were conducted using 2 groups of subjects who received short-term disability benefits for psychiatric disorders. One group (n = 75) was treated in a CMHC program during their disability episode. The comparison group (n = 51) received short-term disability benefits related to psychiatric disorders in the prior year but did not receive CMHC during their disability episode. People in both groups met screening criteria for the CMHC program. Differences in cost and days absent from work were tested using Student t tests and confirmed using nonparametric Wilcoxon rank sum tests. Differences in return to work and transition to long-term disability leave were tested using chi-square tests. The cost-effectiveness analysis used the net benefit regression framework. RESULTS: The results suggest that with CMHC, for every 100 people on short-term disability leave for psychiatric disorders, there could be $50 000 in savings related to disability benefits along with more people returning to work (n = 23), less people transitioning to long-term disability leave (n = 24), and 1600 more workdays. CONCLUSIONS: CMHC models of disability management based on our Canadian data may be a worthwhile investment in helping people who are receiving short-term disability benefits for psychiatric disorders to receive adequate treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".