“It's a Thing”: What Nurse Elders Believe Novice Nurses Need to Know About Managing Emotional Labour
Bibliographic record
Abstract
BACKGROUND: Emotional labour (EL) can take a significant toll on nurses' mental health and well-being and has serious implications for the retention of nurses in the workforce. Nurse educators have struggled to prepare novices to manage EL or find serviceable resources with which to do so within the body of nursing literature, which is dominated by essentially unhelpful narratives and is absent of the practical, real-world wisdom of elder nurses. PURPOSE: This exploratory research study illuminated elder nurses' experiential knowledge and beliefs of what is important for novices to learn about EL. METHODS: Conventional Content Analysis (CCA) of qualitative survey data from 688 nurses with 20+ years of experience. RESULTS: CCA generated descriptive categories and sub-categories: What the elders themselves learned as student nurses, and their advice to novices in the individual realm, ("It's a Thing," healthy disengagement, supporting mental and physical well-being, reframing self-reproach), team realm (peer support, mentors), and institutional realm (structural barriers to mentors' support of novices, building novices' capacities for self-advocacy, resources to support health and well-being). CONCLUSIONS: The elders' data confronted and reframed legacy individuated narratives in the literature by supporting and contextualizing nurses' emotional work. Elders advised novices that EL is a reality requiring concrete strategies to manage it throughout their practices. Elders extended their focus for management of EL beyond the individual to include peer support, mentorship, and the structural conditions in which nurses perform their EL, highlighting the need to empower nurses by building their capacity for self-advocacy of their workers' rights.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".