“Complicated and Overwhelming”: Examining the Mental Health and Well-being of Undergraduate Nursing Students
Bibliographic record
Abstract
Many students report feeling overwhelmed and stressed as they complete their academic programs given their developmental stage. Within nursing education, there are many factors that may affect the mental health and well-being of undergraduate nursing students. The literature regarding mental health and well-being in undergraduate nursing students indicates that academic pressures and poor coping skills often results in high levels of stress and anxiety. This mixed methods study explored the mental health concerns and levels of flourishing of undergraduate nursing students at the University of Calgary. Using an online survey, 159 participants completed the CCAPS-34 questionnaire and Flourishing Scale questions. Participants also answered open ended questions on what aspects of their nursing experiences affected their well-being and what the Faculty of Nursing could do to improve student mental health and well-being outcomes. Overall, undergraduate nursing students showed higher levels of flourishing despite also identifying moderate levels of concerns including depression, anxiety, and stress. Predominant themes related to well-being were that: the high academic workload led to high levels of stress and anxiety, the order of theory and clinical courses negatively impacted students’ learning experiences, the learning environment was often not supportive of students experiencing mental health concerns, and that more could be done by the faculty to increase student wellness. With the implications and considerations of this study, it is possible to make changes in academic programming and curriculum that will increase resiliency and positive copings skills in nursing 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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 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".