How nurses’ use of language creates meaning about healthcare users and nursing practice
Bibliographic record
Abstract
Nursing practice occurs in the context of conversations with healthcare users, other healthcare professionals, and healthcare institutions. This discussion paper draws on symbolic interactionism and Fairclough's method of critical discourse analysis to examine language that nurses use to describe the people in their care and their practice. We discuss how nurses' use of language constructs meaning about healthcare users and their own work. Through language, nurses are articulating what they believe about healthcare users and nursing practice. We argue that the language nurses use can contribute to viewing their practice as tasks on bodies that must be accomplished efficiently and objectively within the biomedical model, rather than relational and person-centered. Moreover, the language nurses use can perpetuate a sense of powerlessness within healthcare systems yet paradoxically they are in a position of power over healthcare users. Nurses' compliance with the efficiency and biomedical model results in a lack of emphasis on the full breadth of nursing work, which could be enacted in relational rather than power-laden practices. We conclude by positing that careful use of language among nurses in all settings is essential, if we are to begin to articulate what nursing is to ourselves and to others.
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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 | 0.042 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.028 | 0.032 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".