Support, Mentorship and Well-Being in Canadian and Croatian Faculties of Education: Professor and Student Perspectives
Bibliographic record
Abstract
The purpose of this quantitative study was to examine professors’ and Master of Education (MEd) students’ well-being, support, academic self-efficacy and mentorship in Canada and Croatia. Overall, 118 professors and 98 MEd students from three universities in Canada and three universities in Croatia completed the online surveys in English and Croatian, respectively. The frameworks of self-determination theory and relational cultural theory informed interpretation of our findings. Results suggest that for professors in both countries, personal support, professional support and academic self-efficacy predict professional well-being. Only personal support predicts personal well-being in Canadian professors, while personal support and academic self-efficacy predicts personal well-being in Croatia. Personal and professional support was also associated with positive mentorship practices in Canada. Students in both countries, who felt supported professionally and personally, reported greater professional and personal well-being respectively. Self-efficacy may make a difference for Croatian students but seemed to have little unique impact on Canadian students. Studying part-time in Canada was associated with higher personal and professional well-being but was associated with lower personal well-being in Croatia. Mentorship practices seemed to have little effect on well-being in either country. Overall, professors reported higher well-being and support than M.Ed. students. We conclude with recommendations that would be informative for university administrators, graduate programs, and services interested in the well-being of professors and graduate students.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".