“In Touch With my Creative Side”: Supporting Self-Care Among Nursing Students Through Arts-Based Pedagogy.
Bibliographic record
Abstract
Nursing students experience academic, practice-related, and personal stressors, all of which can affect their learning, fitness-to-practice, and overall well-being. One way for educators to support students’ well-being is to foster their capacity for, and enactment of, self-care. We used an arts-based assignment that required students to reflect on the stressors they encountered and engage in strategies to relieve their stress and provide self-care. The assignment also provided students with a creative outlet for managing the stressors they faced during their program of study. We designed a web-based survey comprised of Likert scales and three open-ended questions to explore students’ perspectives on the assignment. These open-ended qualitative data and optional submissions of creative assignments are the focus of this paper. The use of an arts-based assignment added positively to the experience for many students; for others, evaluation of their creative work detracted from the experience. Résumé Les étudiantes en sciences infirmières vivent du stress dans le contexte de leurs études universitaires, des pratiques en milieux de soins et de leur vie personnelle, qui peut affecter leur apprentissage, leur aptitude à la pratique et leur bien-être général. Une façon pour les professeures et enseignantes de soutenir le bien-être des étudiantes est de favoriser leur capacité à prendre soin d’elles-mêmes et à la mettre en pratique. Nous avons utilisé un travail faisant appel aux arts qui demandait aux étudiantes de réfléchir aux facteurs de stress auxquels elles étaient confrontées et de s’engager dans des stratégies pour diminuer ce stress et prendre soin d’elles-mêmes. L’exercice s’est également avéré un exutoire créatif pour gérer le stress auquel elles ont été confrontées au cours de leur programme d’études. Nous avons conçu un sondage en ligne composé de questions à répondre à l’aide d’une échelle de Likert et de trois questions ouvertes pour explorer les points de vue des étudiantes sur ce travail. Les données qualitatives en réponse à ces questions ouvertes et les travaux créatifs soumis de manière facultative sont au centre de cet article. L’utilisation d’un travail basé sur les arts a été une expérience positive pour de nombreuses étudiantes; pour d’autres, l’évaluation de leur travail créatif a nui à l’expérience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".