Association Between Patient Sociodemographic and Clinical Characteristics and Acute Mental Health Service Utilization Within One Year Following Enrollment in the Rapid Access and Stabilization Program in Nova Scotia
Bibliographic record
Abstract
Background/Objectives: The Rapid Access and Stabilization Program (RASP), launched in Nova Scotia in April 2023, aims to improve timely psychiatric care, reduce reliance on emergency services, and provide early intervention. This study describes the sociodemographic and clinical characteristics of the RASP participants and examines their association with acute service use. Methods: This cross-sectional descriptive study used self-reported surveys and administrative data from 738 RASP participants. Descriptive statistics summarized key sociodemographic and clinical variables. Associations between these characteristics and acute service use (emergency department visits, inpatient admissions, and mobile crisis calls) were examined using chi-square and Fisher’s Exact tests. Bonferroni correction was applied for multiple comparisons. Results: The sample was predominantly female (65.2%) and aged 20–40 years (38.4%). Despite high rates of severe anxiety (53.9%) and depression (36.0%), acute service use was low: emergency department visits (7.2%), mobile crisis calls (1.0%), and inpatient admissions (0.8%). Preliminary analyses showed that education level and housing status were associated with ED visits and inpatient admissions. However, these associations did not remain statistically significant after Bonferroni correction. Conclusions: Although mental health symptom severity was high, acute mental health service use remained low after RASP enrollment, indicating the program’s potential in reducing reliance on crisis services. No participant characteristics were significantly associated with acute service use after adjustment, underscoring the complexity of predicting utilization and the need for robust multivariable models. Continued investment in rapid access programs may be essential to improving timely mental health care and supporting early intervention strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".