Access and Health System Impact of an Early Intervention Treatment Program for Emerging Adults with Mood and Anxiety Disorders
Bibliographic record
Abstract
OBJECTIVES: Early intervention programs are effective for improving outcomes in first-episode psychosis; however, less is known about their effectiveness for mood and anxiety disorders. We sought to evaluate the impact of an early intervention program for emerging adults with mood and anxiety disorders in the larger health system context, relative to standard care. METHODS: Using health administrative data, we constructed a retrospective cohort of cases of mood and anxiety disorders among emerging adults aged 16 to 25 years in the catchment of the First Episode Mood and Anxiety Program (FEMAP) in London, Ontario, between 2009 and 2014. This cohort was linked to primary data from FEMAP to identify service users. We used proportional hazards models to compare indicators of service use between FEMAP users and a propensity score-matched group of nonusers receiving care elsewhere in the health system. RESULTS: = 490) had more rapid access to a psychiatrist relative to nonusers (hazard ratio [HR], 2.82; 95% confidence interval, 2.45 to 3.26; median time, 16 vs. 71 days). In the year following admission, FEMAP users also had lower rates of emergency department use for mental health reasons (HR, 0.73; 95% CI, 0.53 to 0.99). We did not observe differences in psychiatric hospitalization rates. CONCLUSIONS: An early intervention model of care for mood and anxiety disorders is associated with better access to psychiatric care and lower use of the emergency department. Our findings suggest that early intervention services for mood and anxiety disorders may be beneficial from a health systems perspective, and further research on the effectiveness of this model of care is warranted.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".