Nurses' Uncertainty in Decision‐Making: A Literature Review
Bibliographic record
Abstract
AIM: This paper is a report of the results of a review of the literature conducted with the goal of determining how nurses' clinical uncertainty has been conceptualized in the nursing literature. BACKGROUND: Although existing research has advanced the body of knowledge regarding the concept of uncertainty in decision-making, this has been largely from physicians' viewpoints and from patients' perspectives (patients' uncertainty). Understanding how nurses' experience and act on uncertainty remains relatively unreported. METHOD: A search of Medline, CINAHL, and PubMed databases was conducted to retrieve literature published from 1990 to 2007. The question guiding the literature review was: How has nurses' clinical uncertainty been conceptualized in nursing literature? FINDINGS: Little exploration has been done of nurses' experience of uncertainty in practice. Many investigators have not theorized about the uncertainty in their studies, but have described nurses' uncertainty in the context of clinical decision-making. The findings from these studies indicated that unfamiliarity with the aspects of patient care is a source of uncertainty, and nurses tended to rely on heuristics or on the expertise of colleagues as sources of information for practice decisions. Expressing uncertainties as information needs might help guide information seeking and reduce uncertainty. However, studies indicated that nurses have difficulty recognizing or expressing uncertainties, and as a result, information needs are not recognized and information seeking is not initiated. CONCLUSIONS: A more comprehensive understanding of nurses' uncertainty could lead to the development and implementation of strategies to support nurses in their clinical decision-making and practice. Descriptions are needed about how nurses experience and respond to uncertainty in their practice, and the influence of uncertainty on their information needs and information seeking.
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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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".