Building Comfort With Ambiguity in Nursing Practice
Bibliographic record
Abstract
Current nursing literature recognizes the need to honor the concept of ambiguity. Nurses experience uncertainty with handling or honoring complexity and ambiguity when confronted with times of struggle. Traditional models of care fall short as patients and families define their expectations of the healthcare system. Nurses bear witness to the discomfort caused by the unknown in their daily practice. They are challenged to address their feelings, unsure of what to anticipate, what to say, or how to respond to their patients. Uncertainty diminishes the opportunity for meaningful dialogue between nurses and other people. Nurses attempting to ease the discomfort of ambiguity by providing patients or families with reassurance, offering advice on how to fix problems, or avoiding talking about situations often express dissatisfaction. Nurses should be invited to explore ambiguity and seek understanding through dialogue and nursing knowledge. Encouraging nurses to define the meaningfulness in nursing practice that embraces human science theory will help relieve some of the ambiguity that exists in current practice. This article will explore the concept of ambiguity, highlight how nursing theory based on human science can support practice, and propose recommendations for practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".