Unveiling the Complexity of Chinese Nonengaged Youths' Career‐Related Competencies Through Latent Class Analysis: Examining Their Profiles, Mental Health, and Social Well‐Being
Bibliographic record
Abstract
BACKGROUND: Career-related competencies (CLCs) are essential for nonengaged youths (NEYs) to achieve a successful school-to-work transition. However, there is limited research identifying the characteristics of distinct subgroups based on CLCs. OBJECTIVE: This study aimed to classify patterns of NEYs based on CLCs and to examine differences in demographic characteristics, mental health (i.e., depression and anxiety), and social well-being (i.e., civic engagement, social contribution, and social integration) among the classes identified. METHODS: = 24.65). Latent Class Analysis (LCA) was performed using Mplus to classify CLCs patterns. t-tests and chi-square tests were used to examine differences in demographic characteristics, mental health, and social well-being between subgroups. RESULTS: LCA results indicated that NEYs were grouped into three subgroups: the high CLCs group, the middle CLCs group, and the low CLCs group. The low CLCs group exhibited the lowest performance across all CLC dimensions, the highest prevalence of mental health issues, and the most challenges in social well-being. CONCLUSIONS: The findings highlighted the significance of CLCs for the mental health and social well-being of NEYs during their school-to-work transition. Despite limitations, this study contributed to understanding the subtypes of NEYs regarding CLCs and offered insights for intervention services aimed at enhancing NEYs' CLCs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".