Virtual, In-Person and Hybrid Utilization Patterns of Youth Accessing Integrated Youth Services: A Retrospective Cohort Study of Youth Ages 12–24
Bibliographic record
Abstract
PURPOSE: Youth mental health and substance use issues are rising worldwide, along with barriers to timely, culturally, and developmentally appropriate care. Integrated Youth Services (IYS) offer early intervention through in-person, virtual, and hybrid options. The objectives of this study were to: (1) compare the demographic and clinical characteristics of youth accessing three types of service modalities (virtual, in-person, and hybrid), and (2) understand the service utilization patterns (number and frequency of visits, repeated visits rate, new registrations) among an IYS youth cohort. METHODS: We reviewed records of 41,034 youth aged 12-24 years who accessed an IYS initiative in British Columbia, Canada, from April 2020 to November 2023. Data included demographic and health surveys completed by youth when they registered for services and service records after session completion. Descriptive and inferential statistical analysis methods were used for analysis. RESULTS: Youth accessed IYS through virtual, in-person, and hybrid services. Younger youth (12-14 years) preferred in-person care, while older youth (21-24 years) mostly used virtual services. Virtual users reported higher distress and poorer overall health. Despite being introduced during COVID-19, virtual services remained in use throughout the study. Hybrid users had the highest repeat visit rates. Twenty five percent of all youth said that they would have gone nowhere if it were not for the IYS initiative. DISCUSSION: Findings underscore the importance of diverse IYS delivery models. Decision makers should support hybrid models, maintain strong in-person care, and expand virtual services to improve accessibility and outcomes for youth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".