Influence of Guidance on Occupational Image and Traineeship’s Satisfaction of Vocational Students
Bibliographic record
Abstract
Initial vocational training (VT) in high school consists of short-term programs leading to employment in a skilled trade. To better align training with employment opportunities and to encourage students to stay in the programs until they graduate, most programs include traineeship. Since traineeships involve acquiring skills directly on the job, they require greater involvement of supervisors to guide the trainees. Given the importance of on-the-job guidance in achieving traineeship objectives, this study examines the potential influence of three dimensions of guidance provided by traineeship supervisors -planning, support, and training- on students' job perception (i.e., occupational image) and traineeship satisfaction. Overall, the results provide mixed results, partially supporting the mediation hypothesis suggested by the results of previous studies. Indeed, the results reveal that the quality of the training offered by the supervisor affects subsequent students' satisfaction with traineeship experience. Training has an indirect effect on satisfaction via the occupational image held by students. However, the expected indirect links between the other two dimensions of supervisor guidance -degree of planning and support perceived by the student- and the students' image of their chosen occupation could not be confirmed. The results support the importance of providing quality on-the-job training to students during their studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".