Sociodemographic and Clinical Profiles of Participants in Nova Scotia’s Rapid Access Stabilization Program and Community Mental Health Program: A Comparative Analysis
Bibliographic record
Abstract
Background/Objective: To address the growing demand for mental health services, Nova Scotia Health introduced the Rapid Access Stabilization Program (RASP) through its Mental Health and Addictions Program (MHAP) in April 2023. RASP is designed to help reduce long wait times, frequent emergency department visits, and admissions to provide early intervention for individuals experiencing mental health problems. The RASP focuses on rapid access and early mental health intervention, aiming to prevent the worsening of patients’ symptoms, improve access to psychiatric care, and reduce service pressures on programs like the Community Mental Health Program (CMHP), which provide more extended, ongoing mental health support. This study compared participants’ sociodemographic and clinical profiles in the RASP and the CMHP. Methods: Data were collected from 1392 participants accessing mental health support either through the RASP or CMHP. A comparative analysis of sociodemographic factors (e.g., age, education, and income) and clinical characteristics (e.g., depression, anxiety, resilience, and substance use) was conducted. Chi-square tests and independent sample t-tests were used to evaluate the mean differences between the groups. Results: Significant sociodemographic and clinical differences emerged between the RASP and CMHP participants. The RASP group was older (M = 40.10 vs. 34.52 years) and more socioeconomically stable, with higher rates of employment (55.3% vs. 47.9%) and homeownership (36.5% vs. 17.7%). In contrast, the CMHP group had higher unemployment (25.7% vs. 16.5%) and lower income levels, with 47.5% earning
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".