Improving mental health care transitions for children and youth: a protocol to implement and evaluate an emergency department clinical pathway
Bibliographic record
Abstract
BACKGROUND: While the emergency department (ED) is often a first point of entry for children and youth with mental health (MH) concerns, there is a limited capacity to respond to MH needs in this setting. Child MH systems are typically fragmented among multiple ministries, organizations, and providers. Communication among these groups is often poor, resulting in gaps, particularly in transitions of care, for this vulnerable population. The evidence-based Emergency Department Mental Health Clinical Pathway (EDMHCP) was created with two main goals: (1) to guide risk assessment and disposition decision-making for children and youth presenting to the ED with MH concerns and (2) to provide a streamlined transition to follow-up services with community MH agencies (CMHAs) and other providers. The purpose of this paper is to describe our study protocol to implement and evaluate the EDMHCP. METHODS/DESIGN: This mixed methods health services research project will involve implementation and evaluation of the EDMHCP in four exemplar ED-CMHA dyads. The Theoretical Domains Framework will be used to develop a tailored intervention strategy to implement the EDMHCP. A multiple baseline study design and interrupted time-series analysis will be used to determine if the EDMHCP has improved health care utilization, medical management of the MH problems, and health sector coordination. The primary process outcome will be the proportion of patients with MH-specific recommendations documented in the health record. The primary service outcome will be the proportion of patients receiving the EDMHCP-recommended follow-up at 24-h or at 7 days. Data sources will include qualitative interviews, health record audits, administrative databases, and patient surveys. A concurrent process evaluation will be conducted to assess the degree of variability and fidelity in implementation across the sites. DISCUSSION: This paper presents a novel model for measuring the effects of the EDMHCP. Our development process will identify how the EDMHCP is best implemented among partner organizations to deliver evidence-based risk management of children and youth presenting with MH concerns. More broadly, it will contribute to the body of evidence supporting clinical pathway implementation within novel partnerships. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT02590302 ).
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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 | 0.110 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".