How to Develop Patient Centered Consulting during Workplace Learning in Postgraduate Medical Education? Opening the Black Box Using the Framework of Four Narrative Profiles for Consultation Performance
Timmerman, A.; Pawlikowska, T.; van der Vleuten, C.; Muris, J.
Show abstract
ContextFour narrative profiles were previously developed as an evidence-informed framework for reflection and feedback on consultation performance in medical education. The profiles are grounded in four typologies mapped onto a conceptual framework, using the dimensions of doctor patient interaction (DPI) and medical expertise (ME) to classify overall performance. ObjectiveContent validation of the narrative profiles derived from routine clinical consultations to inform a developmental roadmap for patient-centred consulting. MethodsA qualitative study was performed in Family Medicine (FM) residency training, in which 11 first year and 7 third year FM trainees participated. The same FM assessor (n=11) observed a series of encounters of the trainee every three months during their training year, after which recurrent behaviours were described in a feedback report and overall consultation performance classified in one of the four typologies. Feedback reports (n=56) were categorised for each typology, coded on recurrent behaviours and then compared with the concordant narrative profile content. ResultsWe identified overlapping recurrent behaviours and communication themes between the narrative profiles and feedback reports. For typology 1 (DPI+, ME-), the communication approach shows natural alignment with the patient, more exploration of patient concerns is needed, and the treatment plan needs connection to the reasons for consulting. In typology 2, active listening provides room for patient experience, yet patient centredness risks losing focus, while exploring of reasons for consulting is present. Inadequate responses to patient cues and losing structure in the consultation for typology 3, result in not grounding reassurance in clinical findings. For typology 4, curiosity towards patients concerns, may support understanding of symptoms and expectations. ConclusionA developmental road map for patient centred consulting was outlined, covering balancing medical tasks with exploring patient cues, enacting leadership in agenda management and integrating medical expertise in applied communication, providing focus and language for individual learning trajectories.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Introducing the 4Ps Model of Transitioning to Distance Learning: a convergent mixed methods study conducted during the COVID-19 pandemic 95%
- Evaluating user experience with immersive technology in simulation-based education: a modified Delphi study with qualitative analysis 95%
- Prohibiting Babel - A call for professional remote interpreting services in pre-operation anaesthesia information 94%
Similar papers in this journal
- The preparedness and response to COVID-19 in a quaternary Intensive Care Unit in Australia: perspectives and insights from frontline critical care clinicians 94%
- What is the suitability of clinical vignettes in benchmarking the performance of online symptom checkers? An audit study 94%
- Family physicians supporting patients with palliative care needs within the Patient Medical Home in the community: An Appreciative Inquiry qualitative study 94%
Similar papers in this journal
- Improving capacity for advanced training in obstetric surgery: Evaluation of a blended learning approach 96%
- Effect of introducing interprofessional education concepts on students of various healthcare disciplines in the United Arab Emirates 95%
- Large language models for generating medical examinations: systematic review 95%
Similar papers in this journal
Similar papers in this journal
- Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway 93%
- Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey 92%
- Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.