Youth‐oriented outcomes of education, employment and training interventions for upcoming youth: Protocol for a discrete choice experiment
Bibliographic record
Abstract
AIM: The issue of youth who are not engaged in education, employment or training has been a focus of policymakers for decades. Although interventions exist for these youth, they often measure success in ways that fail to capture what youth seek to gain. The project aims to address this gap by assessing youth-oriented outcomes for interventions targeting upcoming youth. Acknowledging the stigma attached to the deficit-based notion of not engaged in education, employment or training, hereafter we refer to 'upcoming youth', a term coined by youth partners on the project. This study asks what youth want to achieve by participating in an intervention for upcoming youth, with a view to guiding service and research design. METHODS: A mixed-methods discrete choice experiment will be conducted with youth engaged as partners. A qualitative (focus group) stage will be conducted to design discrete-choice experiment attributes and levels. The experiment will be piloted and administered online to approx. 500 youth (aged 14-29) across Canada to identify the outcomes that youth prioritize for interventions. Latent class analyses will then be conducted to explore clusters of outcomes that different groups of youth prioritize. CONCLUSIONS: From a strengths-based recovery-oriented framework, hearing the voices of the target population is important in designing and evaluating services. This youth-oriented research project will identify the intervention outcomes that are the highest priority for upcoming youth. Findings will inform the development, implementation and testing of interventions targeting relevant outcomes for youth who are not engaged in education, employment or training.
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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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.074 | 0.011 |
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".