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. 2025 May 28:8:100356.
doi: 10.1016/j.ijnsa.2025.100356. eCollection 2025 Jun.

Cognitive biases and contextual factors explaining variability in nurses' fall risk judgements: a multi-centre cross-sectional study

Miyuki Takase  1 , Naomi Kisanuki  1 , Yoko Sato  2 , Kazue Mitsunaka  2 , Masako Yamamoto  1
Affiliations

Cognitive biases and contextual factors explaining variability in nurses' fall risk judgements: a multi-centre cross-sectional study

Miyuki Takase et al. Int J Nurs Stud Adv. .

Abstract

Background: Assessing fall risk is a complex process requiring the integration of diverse information and cognitive strategies. Despite this complexity, few studies have explored how nurses make these judgements. Moreover, existing research suggests variability in nurses' fall risk assessments, but the reasons for this variation and its appropriateness remain unclear.

Objective: This study aimed to investigate how nurses judge fall risk, and how cognitive biases and contextual factors are associated with their judgements.

Methods: Using purposive sampling, 335 nurses from six hospitals in western Japan participated in an online survey. The participants rated the likelihood of falls in 18 patient scenarios and completed measures of cognitive bias such as base-rate neglect, belief bias, and availability bias. A linear mixed-effects regression tree was used to identify factors related to their judgements, and a linear mixed-effects regression model examined associations between judgement variability, cognitive biases, and clinical speciality.

Results: Nurses' fall risk assessments were primarily determined by whether patients called for assistance, followed by the use of sleeping pills, the presence of a tube or drain, and patient mobility status. Judgement variability was linked to nurses' gender, education, clinical context/speciality, and susceptibility to availability bias.

Conclusion: Variability in clinical judgement may be justified when reflecting personalised, context-specific care. However, inconsistencies arising from cognitive biases are problematic. Healthcare organisations should offer targeted training to enhance contextual expertise and reduce the influence of cognitive biases on fall risk assessments.

Study registration: Not registered.

Keywords: Clinical speciality; Cognitive bias; Falls; Judgement; Nurses; Regression tree; Risks.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Miyuki Takase reports financial support was provided by Japan Society for the Promotion of Science. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1
Fig. 1
The means and standard deviation of nurses’ judgement of the risk of falling in each scenario.
Fig 2
Fig. 2
Regression tree illustrating nurses’ judgement in the risk of falling. Note: The numeric values in the boxes indicate the mean probability of falling estimated by nurses, and ‘RSS’ indicates the residual sum of squares. N indicates the number of observations (335 × 18 = 6030 observations in total). In this analysis, patient conditions (see Table 1) were dummy-coded, with the risk level 1 variable serving as the reference category for each condition.
Fig 3
Fig. 3
Moderating effects between the risk factors and clinical speciality.
See this image and copyright information in PMC

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