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Self-regulated learning in the clinical context: a systematic review.

ABSTRACT: OBJECTIVES:Research has suggested beneficial effects of self-regulated learning (SRL) for medical students' and residents' workplace-based learning. Ideally, learners go through a cyclic process of setting learning goals, choosing learning strategies and assessing progress towards goals. A clear overview of medical students' and residents' successful key strategies, influential factors and effective interventions to stimulate SRL in the workplace is missing. This systematic review aims to provide an overview of and a theoretical base for effective SRL strategies of medical students and residents for their learning in the clinical context. METHODS:This systematic review was conducted according to the guidelines of the Association for Medical Education in Europe. We systematically searched PubMed, EMBASE, Web of Science, PsycINFO, ERIC and the Cochrane Library from January 1992 to July 2016. Qualitative and quantitative studies were included. Two reviewers independently performed the review process and assessed the methodological quality of included studies. A total of 3341 publications were initially identified and 18 were included in the review. RESULTS:We found diversity in the use of SRL strategies by medical students and residents, which is linked to individual (goal setting), contextual (time pressure, patient care and supervision) and social (supervisors and peers) factors. Three types of intervention were identified (coaching, learning plans and supportive tools). However, all interventions focused on goal setting and monitoring and none on supporting self-evaluation. CONCLUSIONS:Self-regulated learning in the clinical environment is a complex process that results from an interaction between person and context. Future research should focus on unravelling the process of SRL in the clinical context and specifically on how medical students and residents assess their progress towards goals.

SUBMITTER: van Houten-Schat MA

PROVIDER: S-EPMC6175376 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Self-regulated learning in the clinical context: a systematic review.

van Houten-Schat Maaike A MA Berkhout Joris J JJ van Dijk Nynke N Endedijk Maaike D MD Jaarsma A Debbie C ADC Diemers Agnes D AD

Medical education 20180625 10

<h4>Objectives</h4>Research has suggested beneficial effects of self-regulated learning (SRL) for medical students' and residents' workplace-based learning. Ideally, learners go through a cyclic process of setting learning goals, choosing learning strategies and assessing progress towards goals. A clear overview of medical students' and residents' successful key strategies, influential factors and effective interventions to stimulate SRL in the workplace is missing. This systematic review aims t ...[more]

PMID: 29943415

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Project description:ObjectivesThe primary objectives of this study were to examine the regulatory processes of medical students as they completed a diagnostic reasoning task and to examine whether the strategic quality of these regulatory processes were related to short-term and longer-term medical education outcomes.MethodsA self-regulated learning (SRL) microanalytic assessment was administered to 71 second-year medical students while they read a clinical case and worked to formulate the most probable diagnosis. Verbal responses to open-ended questions targeting forethought and performance phase processes of a cyclical model of SRL were recorded verbatim and subsequently coded using a framework from prior research. Descriptive statistics and hierarchical linear regression models were used to examine the relationships between the SRL processes and several outcomes.ResultsMost participants (90%) reported focusing on specific diagnostic reasoning strategies during the task (metacognitive monitoring), but only about one-third of students referenced these strategies (e.g. identifying symptoms, integration) in relation to their task goals and plans for completing the task. After accounting for prior undergraduate achievement and verbal reasoning ability, strategic planning explained significant additional variance in course grade (?R(2 ) = 0.15, p < 0.01), second-year grade point average (?R(2) = 0.14, p < 0.01), United States Medical Licensing Examination Step 1 score (?R(2) = 0.08, p < 0.05) and National Board of Medical Examiner subject examination score in internal medicine (?R(2) = 0.10, p < 0.05).ConclusionsThese findings suggest that most students in the formative stages of learning diagnostic reasoning skills are aware of and think about at least one key diagnostic reasoning process or strategy while solving a clinical case, but a substantially smaller percentage set goals or develop plans that incorporate such strategies. Given that students who developed more strategic plans achieved better outcomes, the potential importance of forethought regulatory processes is underscored.

Project description:BACKGROUND:Clinical narratives represent the main form of communication within health care, providing a personalized account of patient history and assessments, and offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. OBJECTIVE:The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigated the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. METHODS:Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified 110 relevant studies and extracted information about text data used to support machine learning, NLP tasks supported, and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation, and any relevant statistics. RESULTS:The majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents, with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing the predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free-text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable because of the sensitive nature of data considered. Besides the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The majority of studies focused on text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management, and surveillance. CONCLUSIONS:We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which do not require data annotation.

Project description:Self-regulated learning (SRL), which is learners' ability to proactively select and use different strategies to reach learning goals, is associated with academic and clinical success and life-long learning. SRL does not develop automatically in the clinical environment and its development during the preclinical to clinical learning transition has not been quantitatively studied. Our study aims to fill this gap by measuring SRL in medical students during the transitional period and examining its contributing factors.Medical students were invited to complete a questionnaire at the commencement of their first clinical year (T0), and 10 weeks later (T1). The questionnaire included the Motivated Strategies for Learning Questionnaire (MSLQ) and asked about previous clinical experience. Information about the student's background, demographic characteristics and first clinical rotation were also gathered.Of 118 students invited to participate, complete paired responses were obtained from 72 medical students (response rate 61%). At T1, extrinsic goal orientation increased and was associated with gender (males were more likely to increase extrinsic goal orientation) and type of first attachment (critical care and community based attachments, compared to hospital ward based attachments). Metacognitive self-regulation decreased at T1 and was negatively associated with previous clinical experience.Measurable changes in self-regulated learning occur during the transition from preclinical learning to clinical immersion, particularly in the domains of extrinsic goal orientation and metacognitive self-regulation. Self-determination theory offers possible explanations for this finding which have practical implications and point the way to future research. In addition, interventions to promote metacognition before the clinical immersion may assist in preserving SRL during the transition and thus promote life-long learning skills in preparation for real-world practice.

Project description:Self-generation of knowledge can activate deeper cognitive processing and improve long-term retention compared to the passive reception of information. It plays a distinctive role within the concept of inquiry-based learning, which is an activity-oriented, student-centered collaborative learning approach in which students become actively involved in knowledge construction by following an idealized hypothetico-deductive method. This approach allows students to not only acquire content knowledge, but also an understanding of investigative procedures/inquiry skills - in particular the control-of-variables strategy (CVS). From the perspective of cognitive load theory, generating answers and solutions during inquiry-based learning is inefficient as it imposes an intrinsic and extraneous load on learners. Previous research on self-generation of content knowledge in inquiry-based learning has demonstrated that (1) a high cognitive load impairs retention of the generated information, (2) feedback is a fundamental requirement for self-generation of complex content knowledge, (3) self-generation success is key to long-term retention, and (4) generating and rereading place different demands on learners. However, there is still no research on the self-generation of scientific reasoning skills (procedural knowledge) and no knowledge of interaction between the (long-term) retention of these skills with prior knowledge, feedback and self-generation success. That is why this experiment was conducted. The focus of this research is to analyze the distinctive role of self-generation of scientific reasoning skills within the concept of inquiry-based learning and to identify the influence of prior knowledge and self-generation success on short-term and long-term retention. For this purpose, an experiment involving 133 6th and 7th graders was conducted. An inquiry activity that included the self-generation of scientific reasoning skills was compared to an inquiry task that had students simply read information about the experimental design. We used both an immediate and a delayed test to examine which treatment better developed a deeper understanding of CVS and an ability to apply this knowledge to novel problems (transfer). Direct instruction was clearly superior to self-generation in facilitating students' acquisition of CVS immediately after the inquiry task. However, after a period of 1 week had elapsed, both treatment conditions turned out to be equally effective. A generation effect was only found among students with high self-generation success after a 1-week delay.

Project description:BackgroundTimely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.ObjectiveThis study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning-based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings.MethodsPubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to "vital signs," "clinical deterioration," and "machine learning." Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines.ResultsWe identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97.ConclusionsIn studies that compared performance, reported results suggest that machine learning-based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

Dataset Information

Self-regulated learning in the clinical context: a systematic review.

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Self-regulated learning in the clinical context: a systematic review.

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