Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada
Bibliographic record
Abstract
BACKGROUND: Prior research has showed the importance of providing integrated support services to prevent and reduce youth not in education, employment, or training (NEET) related challenges. There is limited evidence on NEET youth's perspectives and preferences for employment, education, and training services. The objective of this study was to identify employment, education and training service preferences of NEET youth. We acknowledge the deficit-based lens associated with the term NEET and use 'upcoming youth' to refer to this population group. METHODS: Canadian youth (14-29 years) who reported Upcoming status or at-risk of Upcoming status were recruited to the study. We used a discrete choice experiment (DCE) survey, which included ten attributes with three levels each indicating service characteristics. Sawtooth software was used to design and administer the DCE. Participants also provided demographic information and completed the Global Appraisal of Individual Needs-Short Screener. We analyzed the data using hierarchical Bayesian methods to determine service attribute importance and latent class analyses to identify groups of participants with similar service preferences. RESULTS: A total of n=503 youth participated in the study. 51% of participants were 24-29 years of age; 18.7% identified as having Upcoming status; 41.1% were from rural areas; and 36.0% of youth stated that they met basic needs with a little left. Participants strongly preferred services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Three latent classes were identified and included: (i) job and educational services (38.9%), or services that include career counseling and securing a work or educational placement; (ii) mental health and wellness services (34.9%), or services that offer support for mental health and wellness in the workplace and free mental health and substance use services; and (iii) holistic skills building services (26.1%), or services that endorsed skills for school and job success, and life skills. CONCLUSIONS: This study identified employment, education, and training service preferences among Upcoming youth. The findings indicate a need to create a service model that supports holistic skills building, mental health and wellness, and long-term school and job opportunities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".