Dataset Information

Improving Clinical Nurses' Development of Supervision Skills through an Action Learning Approach.

ABSTRACT: The aim of this study was to investigate action learning as an implementation method in a large-scale project with many participants in several autonomous and geographically spread groups. The focus of the implementation was the Model of Practical Skill Performance as a learning and supervision tool in the clinical education of nursing students. Nineteen action learning groups were established, and a total of 129 clinical supervisors and 13 facilitators were involved. To evaluate the implementation process, qualitative data were generated through three focus group interviews, questionnaires, and notes. Data illuminate clinical supervisors' perceptions of value, impact, and sustainability when they participate in an action learning group to become familiar with the Model of Practical Skill Performance. The deductive data analysis was guided by central concepts from action learning. Action learning proved to be an engaging and effective tool in the implementation where the main strength seemed to be the autonomous local group supporting collective reflections on actions. Clinical supervisors had the right competences to adopt a reflective process-oriented approach, which is the hallmark of action learning. This study shows the necessity of collaboration between stakeholders in practice, education, and management to implement large-scale projects in clinical practice. The findings imply that managers should choose participants on the basis of their motivation and their voluntary wish to participate and that nurses' immersion in the project over time aids implementation.

SUBMITTER: Sommer I

PROVIDER: S-EPMC7049863 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Improving Clinical Nurses' Development of Supervision Skills through an Action Learning Approach.

Sommer Irene I Larsen Karin K Nielsen Carsten M CM Stenholt Britta V BV Bjørk Ida Torunn IT

Nursing research and practice 20200219

The aim of this study was to investigate action learning as an implementation method in a large-scale project with many participants in several autonomous and geographically spread groups. The focus of the implementation was the Model of Practical Skill Performance as a learning and supervision tool in the clinical education of nursing students. Nineteen action learning groups were established, and a total of 129 clinical supervisors and 13 facilitators were involved. To evaluate the implementat ...[more]

PMID: 32148957

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Project description:BACKGROUND:Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms. OBJECTIVE:The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML. METHODS:To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death. RESULTS:With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients. CONCLUSIONS:Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care.

Project description:Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

Dataset Information

Improving Clinical Nurses' Development of Supervision Skills through an Action Learning Approach.

Publications

Improving Clinical Nurses' Development of Supervision Skills through an Action Learning Approach.

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