A New Acute-At-Home Child and Adolescent Clinical Service: Evaluation of Impact
Bibliographic record
Abstract
OBJECTIVE: An Acute at Home (AAH) clinical service was implemented to reduce emergency and inpatient admissions to the regional tertiary child and adolescent mental health system. This paper examines describes the served the population and impact on emergency and inpatient admissions. METHODS: Analysis of re-admission rates for those enrolled before and after the May 2019 implementation of the AAH service in comparison over the same time period to an unexposed comparison group. In addition the groups were compared on clinical and demographic variables comparing those exposed and those not exposed to the AAH service. RESULTS: The results indicated that the AAH group experienced reduced rates of readmission and lengths of stay post-exposure. Family composition, sex, seven Adverse Childhood Experience survey items, and nine Western Canada Waitlist Child Mental Health Priority Criteria Score items distinguished those exposed to AAH compared to those who were not. Thirteen of 19 independent variables indicated greater pathology in the AAH group with less likelihood of potential of danger to self. CONCLUSION: The present results indicate a substantial benefit of the AAH service via reducing readmissions and lengths of stay. The quantitative measures warrant a careful qualitative examination of the AAH processes along with ongoing monitoring of the program's effect.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".