“We Have Bills to Pay Too”: The Juggling Act of Working While Attending a School of Nursing
Bibliographic record
Abstract
Background: Nursing students often engage in paid employment. There is a lack of Canadian research exploring the incidence and effects of working on nursing students. Objectives: We explored the factors involved in nursing students participating in paid employment while attending school; the incidence of students who work during the semester; the characteristics of students’ work patterns (nature of the job, hours of work, rate of pay, why they work, other sources of income); and student perceptions of how work impacts their academic/professional development and personal lives. Design: This exploratory descriptive study used the Paid Work Questionnaire. Setting: The study was conducted at an Atlantic Canadian school of nursing in the 2019–2020 school year. Participants: A total of 128 nursing students completed the questionnaire. Data from the 71 students who indicated that they were working was further analyzed. Methods: Quantitative data were explored using descriptive statistics, and thematic analysis was used to analyze qualitative data. Results: Just over half of participants reported working while attending a school of nursing. Results provided insight into the types of common student jobs and hours worked. Employment, although often necessary, has been shown to influence a student’s perception of their academic success. Implications for patient safety as well as school of nursing attrition were also identified. Additional challenges of working, as derived from qualitative data, include effects on work–life balance, academic success, and stress. Results also highlighted benefits of working, such as effects on socialization and professional development/skills and attributes, as well as financial benefits. Conclusions: Educators and governments have a role to play in supporting nursing students and addressing the challenges experienced by students who work. Students can benefit from engaging in paid employment; however, creative solutions related to educational and financial support should be explored.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".