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Exploring residents' communication learning process in the workplace: a five-phase model.

ABSTRACT: Competency-based education is a resurgent paradigm in professional medical education. However, more specific knowledge is needed about the learning process of such competencies, since they consist of complex skills. We chose to focus on the competency of skilled communication and want to further explore its learning process, since it is regarded as a main competency in medical education.This study aims to explore in more detail the learning process that residents in general practice go through during workplace-based learning in order to become skilled communicators.A qualitative study was conducted in which twelve GP residents were observed during their regular consultations, and were interviewed in-depth afterwards.Analysis of the data resulted in the construction of five phases and two overall conditions to describe the development towards becoming a skilled communicator: Confrontation with (un)desired behaviour or clinical outcomes was the first phase. Becoming conscious of one's own behaviour and changing the underlying frame of reference formed the second phase. The third phase consisted of the search for alternative behaviour. In the fourth phase, personalization of the alternative behaviour had to occur, this was perceived as difficult and required much time. Finally, the fifth phase concerned full internalization of the new behaviour, which by then had become an integrated part of the residents' clinical repertoire. Safety and cognitive & emotional space were labelled as overall conditions influencing this learning process.Knowledge and awareness of these five phases can be used to adjust medical working and learning environments in such a way that development of skilled medical communication can come to full fruition and its benefits are more fully reaped.

SUBMITTER: van den Eertwegh V

PROVIDER: S-EPMC4441458 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Project description:ObjectivesWe expect physicians to be lifelong learners. Participation in clinical practice is an important potential source of that learning. To support physicians in this process, a better understanding of how they learn in clinical practice is necessary. This study investigates how physicians recognise and use informal feedback from interactions with patients in outpatient settings as learning cues to adjust their communication behaviours in daily practice.MethodsTo understand physicians' use of informal feedback, we combined non-participant observations with semi-structured interviews. We enrolled 10 respiratory physicians and observed 100 physician-patient interactions at two teaching hospitals in the Netherlands. Data collection and analysis were performed iteratively according to the principles of constructivist grounded theory.ResultsFollowing stages of open, axial and selective coding, we were able to conceptualise how physicians use cues to reflect on and adjust their communication. In addition to vast variations within and across patient encounters, we observed recurring adjustments in physicians' communication behaviours in response to specific informal feedback cues. Physicians recognised and used these cues to self-monitor communication performance. They had established 'communication repertoires' based on multiple patient interactions, which many saw as learning opportunities contributing to the development of expertise. Our findings, however, show differences in physicians' individual levels of sensitivity in recognising and using learning opportunities in daily practice, which were further influenced by contextual, personal and interpersonal factors. Whereas some described themselves as having little inclination to change, others used critical incidents to fine-tune their communication repertoires, and yet others constantly reshaped them, seeking learning opportunities in their daily work.ConclusionsThere is large variation in how physicians use learning cues from daily practice. To enhance learning in and from daily practice, we propose turning workplace learning into a collaborative effort with the aim of increasing awareness and the use of informal performance-relevant feedback.

Project description:Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentally using recombinant enzymes, human liver microsome, and animal models. Unfortunately, these experiments are expensive, time-consuming, and often extrapolate poorly to humans because they fail to capture the full breadth of metabolic reactions observed in vivo. As a result, metabolic pathways leading to the formation of toxic metabolites are often missed during drug development, giving rise to costly failures. To address some of these limitations, computational metabolism models can rapidly and cost-effectively predict sites of metabolism-the atoms or bonds which undergo enzymatic modifications-on thousands of drug candidates, thereby improving the likelihood of discovering metabolic transformations forming toxic metabolites. However, current computational metabolism models are often unable to predict the specific metabolites formed by metabolism at certain sites. Identification of reaction type is a key step toward metabolite prediction. Phase I enzymes, which are responsible for the metabolism of more than 90% of FDA approved drugs, catalyze highly diverse types of reactions and produce metabolites with substantial structural variability. Without knowledge of potential metabolite structures, medicinal chemists cannot differentiate harmful metabolic transformations from beneficial ones. To address this shortcoming, we propose a system for simultaneously labeling sites of metabolism and reaction types, by classifying them into five key reaction classes: stable and unstable oxidations, dehydrogenation, hydrolysis, and reduction. These classes unambiguously identify 21 types of phase I reactions, which cover 92.3% of known reactions in our database. We used this labeling system to train a neural network model of phase I metabolism on a literature-derived data set encompassing 20 736 human phase I metabolic reactions. Our model, Rainbow XenoSite, was able to identify reaction-type specific sites of metabolism with a cross-validated accuracy of 97.1% area under the receiver operator curve. Rainbow XenoSite with five-color and combined output is available for use free and online through our secure server at http://swami.wustl.edu/xenosite/p/phase1_rainbow.

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Exploring residents' communication learning process in the workplace: a five-phase model.

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