Learning from experiences of peer support at an integrated youth service organization : a qualitative study
Bibliographic record
Abstract
Mental health challenges are the leading cause of disability in youth worldwide. In Canada, mental health challenges affect one in five youth by age 24. When youth activate mental health services, they reduce their likelihood of experiencing problems later in life, yet less than 20% of youth with mental health challenges are accessing these services. This gap is largely attributed to a lack of comprehensive, well-designed health care services to meet the needs of this age group effectively. Canada’s mental health strategy identifies peer support as an essential component of mental health services. However, the majority of peer support research has focused on adult populations. Largely missing from the literature is an understanding of peer support in a youth mental health context. The purpose of this research was to learn from experiences of peer support at an integrated youth services (IYS) organization in British Columbia (BC), Canada. This study utilized interpretive description methodology and a research community partnership model. Data were collected from IYS centers across BC in urban, rural, and Northern settings. Qualitative, semi-structured interviews were conducted and analyzed to examine how peer support is experienced by: youth peer support workers (YPSWs), service providers, and youth recipients of peer support. Findings are presented within each group of participants. A discussion of similarities and differences across groups illustrates key priorities to advance youth peer support in a meaningful way. Four recommendations to guide IYS organizations in strengthening their youth peer support services are provided: 1) Standardize YPSW hiring and onboarding processes across the IYS network; 2) Establish senior peer support roles and regional peer support practice leads; 3) Implement an evidence-based model of supervision for YPSWs; and 4) Increase the availability and accessibility of YPSWs across BC. Strategies to achieve each recommendation are discussed. As the need for mental health services for youth continues to rise, developing and implementing recovery-oriented and evidence-based interventions is crucial. This study aimed to generate new knowledge to optimize and scale peer support at IYS organizations and to guide broader policies for mental health services offered to youth and by youth in BC, and beyond.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".