Cognitive and implicit biases in nurses' judgment and decision-making: A scoping review
Bibliographic record
Abstract
BACKGROUND: Cognitive and implicit biases of healthcare providers can lead to adverse events in healthcare and have been identified as a patient safety concern. Most research on the impact of these systematic errors in judgment has been focused on diagnostic decision-making, primarily by physicians. As the largest component of the workforce, nurses make numerous decisions that affect patient outcomes; however, literature on nurses' clinical judgment often overlooks the potential impact of bias on these decisions. The aim of this study was to map the evidence and key concepts related to bias in nurses' judgment and decision-making, including interventions to correct or overcome these biases. METHODS: We conducted a scoping review using Joanna Briggs methodology. In November 2020 we searched CINAHL, PsychInfo, and PubMed databases to identify relevant literature. Inclusion criteria were primary research about nurses' bias; evidence of a nursing decision or action; and English language. No date or geographic limitations were set. RESULTS: We found 77 items that met the inclusion criteria. Over half of these items were published in the last 12 years. Most research focused on implicit biases related to racial/ethnic identity, obesity, and gender; other articles examined confirmation, attribution, anchoring, and hindsight biases. Some articles examined heuristics and were included if they described the process of, and the problems with, nurse decision-making. Only 5 studies tested interventions to overcome or correct biases. 61 of the studies relied on vignettes, surveys, or recall methods, rather than examining real-world nursing practice. This could be a serious oversight because contextual factors such as cognitive load, which have a significant impact on judgment and decision-making, are not necessarily captured with vignette or survey studies. Furthermore, survey and vignette studies make it difficult to quantify the impact of these biases in the healthcare system. CONCLUSIONS: Given the serious effects that bias has on nurses' clinical judgment, and thereby patient outcomes, a concerted, systematic effort to identify and test debiasing strategies in real-world nursing settings is needed. TWEETABLE ABSTRACT: Bias affects nurses' clinical judgment - we need to know how to fix it.
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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".