Finding Meaning in the Work of Nursing: An International Study
Bibliographic record
Abstract
Sixty nurses from five countries (Canada, India, Ireland, Japan, and Korea) took part in 11 focus groups that discussed the question: Do you consider your work meaningful? Fostering meaning and mentorship as part of the institutional culture was a central theme that emerged from the discussions. In this article, we begin with a background discussion of meaning and meaningful work as presented in the literature related to existentialism and hardiness. Next, we describe the method and analysis processes we used in our qualitative study asking how nurses find meaning in their very challenging work and report our findings of four themes that emerged from the comments shared by nurses, specifically relationships, compassionate caring, identity, and a mentoring culture. After offering a discussion of our findings and noting the limitations of this qualitative study, we conclude that nursing leaders and a culture of mentorship play an important role in fostering meaningful work and developing hardy employees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".