Improving access to acute mental health services in a general hospital
Bibliographic record
Abstract
BACKGROUND: There is a paucity of service research on the effectiveness of short-term mental health clinics. AIMS: To outline the development of the Urgent Consultation Clinic (UCC), an inter-professional, short-term, mental health program in a general hospital, and to evaluate the effectiveness of the UCC from a quality improvement perspective. METHOD: Participants (n = 143) completed a battery of validated measures assessing psychological and physical symptoms, quality of life, life satisfaction, and satisfaction with services at three time-points. Inter-professional team members rated participants' overall functioning and severity of mental health problems at intake and termination. RESULTS: The median time from referral to initial UCC visit was 12 days. A significant decline in the severity of mental health symptoms was observed, with 87% of participants reporting clinically elevated symptoms at intake compared to 71% at termination. Significant improvements were observed in life satisfaction, overall functioning, and mental quality of life. Sixty-nine percent of participants rated the quality of services as good or excellent. CONCLUSIONS: The UCC model of care contributed to improved access to psychiatric evaluation and short-term treatment. This inter-professional model could be applied to other health care settings to meet the needs of patients requiring acute psychiatric services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".