Balancing Act: Exploring the Impact of Student Employment on First-Year Persistence in Postsecondary Education
Bibliographic record
Abstract
This study examines the influence of student employment on the persistence of first-year undergraduate students in postsecondary education. Using a mixed-methods approach, I explore the experiences and perspectives of employed first-year students at the University of Toronto through semi-structured interviews with nine full-time employed first-year students and a follow-up survey capturing both qualitative and quantitative information at the conclusion of their first year. I used deductive and inductive thematic data analysis to identify key themes and patterns related to employment, academic performance, institutional belonging, time management, and engagement. This study reveals that participants persisted towards their second year, but each reported unique experiences balancing work and school. The findings highlight the importance of considering students’ lived experiences balancing work and school within the broader context of their educational journey to inform institutional policies and practices aimed at enhancing the overall academic experience and rates of persistence for undergraduate students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".