Emotional competence and help-seeking intentions as predictors of educational success in vocational training students
Bibliographic record
Abstract
Given the high prevalence of psychological distress among vocational training (VT) students, this study aimed to assess the role of interpersonal emotional competence as a resilience factor promoting the educational success of this population. We postulated that emotional competence would promote educational success, both directly and indirectly by fostering students’ help-seeking intentions when facing a personal or school-related problem. To test these hypotheses, we used a sample of 219 VT students from the Canadian province of Quebec (68% women, M age = 24.58; SD age = 7.95) enrolled in various programs (e.g. institutional and home care assistance, welding and fitting, secretarial studies, and professional cooking). These students were assessed two times, during the first half of their training and again after their training. Results from structural equation modelling revealed that emotional competence was a positive predictor of help-seeking intentions and educational success. However, having the intention to seek help did not translate into higher levels of educational success. Overall, these results highlight the importance of supporting VT students in the development and strengthening of their emotional competence to promote their educational success. Future research is needed to further understand the help-seeking process among VT students and its implications for their academic outcomes.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".