Wait Time Impact of Co-Located Primary Care Mental Health Services: The Effect of Adding Collaborative Care in Northern Ontario
Bibliographic record
Abstract
OBJECTIVES: In the shared care model, psychiatrists and physicians work in the same office areas, write their notes in the same casebooks, and can more rapidly exchange information about referrals and health conditions of their patients. We evaluated the impact of the introduction of a shared mental health care service, co-located with a primary care site, on wait times for mental health services in a northern Ontario city. METHOD: Chart reviews were conducted to examine a total of 3589 referrals for 5 mental health outpatient services (1 shared care and 4 existing services) from January 2001 to the end of June 2004. The shared mental health care service site was started in July 2001. Wait time was measured 6 months prior to and up to 3 years after the introduction of the shared care service. RESULTS: The shared care site offered services more than 40 days sooner and also helped to reduce wait time on the nonshared care sites. After shared care began, the pre-existing, nonshared care services had wait times of about 13 days shorter during the 3 subsequent years. CONCLUSIONS: The shared care service maintained the lowest overall wait times, compared with the existing nonshared care services. The existing services experienced a decrease in the number of days waiting when the baseline wait time was compared with that of the following year.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".