Investigating the impact of cognitive bias in nursing documentation on decision‐making and judgement
Bibliographic record
Abstract
The clinical documentation of patients' mental status, behaviour and functioning is a fundamental aspect of inpatient mental health care. It is an important source of information-sharing with the interprofessional team and used by other clinicians within the circle of care to guide their decision-making process. Given the body of evidence highlighting concerns about the quality of nursing documentation and the growing literature demonstrating the presence of bias in healthcare, it is critically important that we examine the impact of this bias in nursing practice. The primary objective of this study was to determine whether clinical decisions and judgements change when nurses read documentation that is either biased or neutral. Using a quantitative, observational study that used surveys to collect data, participants were exposed to two patient vignettes and six clinical notes associated with each patient (notes were written with either biased or neutral language) and asked to make clinical decisions and judgements. Results from 199 nurse participants from a tertiary mental health hospital revealed a notable relationship between the type of notes read (biased vs. neutral) and clinical practice, namely, participants reading biased notes were less likely to offer health teaching when administering pro re nata (PRN) medication for sleep. We also found differences in decision-making and judgements based on the type of note read depending on years of experience and type of education. The results indicate that biased language in nursing documentation can influence other clinicians' decisions and judgements about patients, thereby indicating a cascade of bias.
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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 | 0.149 | 0.507 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".