14 The dynamics of co-designing a hybrid realist-participatory evaluation within youth peer support services
Bibliographic record
Abstract
Peer support services are grounded in the approach of involving ‘peers’ or individuals who share key lived experiences with clients. Although there is an expanding body of literature focused on youth peer support, there continues to be a need to identify the underlying mechanisms that promote positive impacts within these interventions. This presentation describes the process of developing a hybrid realist and participatory evaluation to examine peer support services for youth with complex mental health and substance use challenges. The study took place in the context of the Transitional Aged Youth program implemented by LOFT Community Services that supports youth between the ages of 14–26. This program engages and utilizes young people who were previous clients of LOFT as peer support workers to help design and facilitate programming to support clients in developing life skills, autonomy and wellness, regardless of the complexity of the challenges they may be facing. The focus of the presentation is threefold. First, we will discuss the development and dynamics of forming an engaging and ethical research partnership between primary investigator and peer researcher. Then we will discuss how this influenced the overall design of the study and related findings. Finally, we will review the lessons learned and implications for the findings and other related research.
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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 | 0.213 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".