Balancing Nurture and Rigour: Seeking Effective Support for Nursing Students in Distress
Bibliographic record
Abstract
As higher educational institutions face a growing demand to graduate more nurses, and mental health and life stresses are recognized as increasing obstacles to student success, the timing is right for nursing programs to evaluate their traditionally rigorous program cultures. At Sunrise University in Western Canada, nursing students make up a large population seeking support services, and there is an increasing need for capacity building in faculty to support learners who are in distress. In this organizational improvement plan (OIP), I explore what can be enhanced or further developed to create more effective support for students who are in distress or who are notably struggling. Early recognition of distress can prevent issues from escalating and, in turn, promote retention, ability to learn, social justice, and student wellness. To achieve this desired state, which aligns with Sunrise University’s strategic plan, I propose the creation of a professional learning community to collaborate with faculty to bring awareness about distress while also nurturing their well-being amidst heavy workloads. Through transformative and shared leadership approaches, this OIP is framed by critical and systems organizational theories, with intersectional and cultural theoretical lenses. The ADKAR change model is used to develop a strategic implementation plan, with appreciative and PDSA inquiry cycles woven through as we monitor and evaluate progress. Future considerations include how to move toward progression policy change and collaboration with the healthcare system to influence a supportive and caring learning environment.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".