Exploring How to Enhance Care and Pathways Between the Emergency Department and Integrated Youth Services for Young People With Mental Health and Substance Use Concerns.
Bibliographic record
Abstract
Abstract Background: Integrated youth services (IYS) provide multidisciplinary care (including mental, physical, and social) prioritizing the needs of young people and their families. Despite a significant rise in emergency department (ED) visits by young Canadians with mental health and substance use (MHSU) concerns over the last decade, there remains a profound disconnect between EDs and MHSU integrated youth services. The first objective of this study was to better understand the assessment, treatment, and referral of young people (ages 12-24 years) presenting to the ED with MHSU concerns. The second objective was to explore how to improve the transition from the ED to IYS for young people with MHSU concerns. Methods: We conducted semi-structured one-on-one video and phone interviews with stakeholders in British Columbia, Canada in the summer of 2020. Snowball sampling was utilized, and participants (n=26) were reached, including ED physicians (n=6), social workers (n=4), nurses (n=2), an occupational therapist (n=1); a counselor (n=1); staff/leadership in IYS organizations (n=4); mental health/family workers (n=3); peer support workers (n=2), and parents (n=3). A thematic analysis (TA) was conducted using a deductive and inductive approach conceptually guided by the Social Ecological Model.Results: We identified three overarching themes, and factors to consider at all levels of the Social Ecological Model. At the interpersonal level inadequate communication between ED staff and young people affected overall care and contributed to negative experiences. At the organizational level, we identified considerations for assessments and the ED and the hospital (wait times, staffing issues, and the physical space). At the community level, the environment of IYS and other community services were important including wait times and hours of operation. Policy level factors identified include inadequate communication between services (e.g., different charting systems and documentation). Conclusions: This study provides insight into important long-term systemic issues and more immediate factors that need to be addressed to improve the delivery of care for young people with MHSU challenges. This research supports intervention development and implementation in the ED for young people with MHSU concerns.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".