Exploring an Alternative Metaphor for Nursing: Relinquishing Military Images and Language
Bibliographic record
Abstract
The language used to describe nursing practice and nursing leadership has a profound influence on how nurses think about themselves, their work relationships, and indeed the very essence of their reason for being. Language often includes metaphor in order to help capture the complexities and layers of meaning that establish contexts for action. Nurses and others have relied on various metaphors to describe nursing work. However, there is one metaphor that, more than any other, has shaped the context of nursing work and formed the images and the meanings that nurses have of themselves and their purposes in practice. The privileged one is the military metaphor. This article explores the notion of metaphor, and its usefulness and potential to help nurses change their work patterns. The traditions and history of the military metaphor are examined and an alternative notion of the "frontier" is proposed in order to enhance understanding of the potential for change.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".