Final-Year Student Burnout in Career Navigation: The Demand-Resource Model Approach
Bibliographic record
Abstract
Student burnout is a significant problem in higher education, positively linked to psychological distress and a higher likelihood of potential mental health issues such as anxiety and depression. The demand-resource model, a theoretical framework examining both the salutogenic and pathogenic effects within the university context on students’ well-being, will be adapted to look at specific stressors from job and graduate school applications. Although many studies have been established on both job and student burnout, it is less explored in the context of career search transition phrases. The study employs a cross-sectional observational design, participants are final-year undergraduate students in any disciplines at the University of Toronto. Students will complete an online demand-resource questionnaire containing scales on social and personal factors, as well as questions pertaining to burnout, engagement, and related outcomes. Findings will inform university faculties in designing curriculums and interventions to develop a healthier student body, enhance engagement, and prevent long-time health detriments resulting from burnout.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".