Nurses' ways of talking about their experiences of (in)justice in healthcare organizations: Locating the use of language as a means of analysis
Bibliographic record
Abstract
Nurses have their own ways of talking about their experiences of injustice in healthcare organizations. The aim of this article is to describe how nurses talk about their work-life experiences and discuss the discursive effects that arise from nurses' use of language regarding their political agency. To this end, we present the findings garnered from a study focused on exploring how nurses deploy their political agency to project their idea of social and political justice in public healthcare organizations and how they face the challenges and uncertainties of (re)thinking their institutional order when it does not resonate with their professional ethos. We then discuss the implications that nurses' use of language has in relation to their ability to deploy their political agency to oppose the forms of injustice they face in their daily practice. We conclude by stating that careful attention should be placed on understanding the discursive implications of nurses' use of language on their individual and collective emancipation in healthcare organizations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".