"Stop the Noise!" From Voice to Silence
Bibliographic record
Abstract
Nurses are frequently portrayed in the literature as being silent about ethical concerns that arise in their practice. This silence is often represented as a lack of voice. However, in our study, we found that nurses who responded to questions about moral distress were not so much silent as silenced. These nurses were enacting their moral agency by engaging in diverse, multiple and time-consuming actions in response to situations identified as morally distressing with families, colleagues, physicians, educators or managers. In many situations, they took action by contacting other healthcare team members, making referrals and coordinating care with other departments such as home care and hospice, as well as initiating contact with groups such as professional regulatory bodies or unions. Examining the relationship between ethical climate, moral distress and voice offers insights into both the meaning and impact of being silenced in the workplace.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".