Balancing Act: How students navigate work and study in Canada
Bibliographic record
Abstract
Education is essential for personal and societal development, yet balancing academic responsibilities and employment remains challenging for students. This thesis investigates the complexities of work-study balance among Canadian students, encompassing both domestic and international cohorts. Utilizing JD-R (Job Demands Resources) theory and transactional theory of stress and its extension ie the Challege-Hindrance Framework, the thesis examines how work demands like perceived work overload and study demands like minimum credit requirements influence perceived degree completion. A quantitative online survey gathered data from 108 participants across various academic disciplines and employment sectors, including domestic and international students. Findings reveal that while perceived work overload alone does not directly predict degree completion, its impact is mediated by work-study conflict. International student status, despite unique challenges like visa restrictions, does not significantly alter this mediated relationship. Practically, the thesis underscores the need for tailored support mechanisms and flexible policies in educational institutions to foster a supportive work-study environment. The study acknowledges limitations in sample size and design, suggesting that future research employs longitudinal and mixed methods approaches to further explore these dynamics across diverse academic and cultural contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".