Safeguarding student well-being: establishing a respectful learning environment in undergraduate psychiatric/mental health education
Bibliographic record
Abstract
Accessible summary Significance: Minimal attention has been devoted to the prevention and management of psychiatric/mental health student nurse distress. The well-being of these students has major implications for learners, the learning environment and prospective patients. This manuscript: • consolidates and synthesizes the literature pertaining to the emotional well-being of undergraduate psychiatric/mental health student nurse well-being; • discusses the precursors and implications associated with student nurse distress; • offers practical strategies. The emotional well-being of psychiatric/mental health student nurses is critical to learners, the educational process and ultimately prospective patients. However, with a focus on client care, the psychological disposition and needs of psychiatric/mental health student nurses can be inadvertently marginalized or overlooked. To augment the extant literature, this paper examines how a respectful learning environment can be instrumental in safeguarding the emotional well-being of learners. Towards this end, this paper synthesizes and consolidates the literature regarding undergraduate psychiatric/mental health student nurse well-being, offers suggestions towards the establishment of a respectful learning environment, and invites further dialogue regarding this salient issue.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".