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Record W6983675967

Narrative Shifts Prompt the Development of Adaptive Expertise in Pediatric Subspecialty Residents

2018· article· en· W6983675967 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsGrounded theorySubspecialtyExcellenceCurriculumNarrativeData collectionHealth care
DOInot available

Abstract

fetched live from OpenAlex

In an era of increasing complexity in healthcare, it is necessary for physicians to be flexible and adaptive in their use of knowledge and experience to solve new, unexpected and multifaceted problems in clinical practice. This complex problem solving is known to be an essential skill for adaptive experts and is the standard of excellence in training future health care professionals. Despite its importance for medical training, little is known about how adaptive expertise develops in medical trainees. Specifically, we do not know how residents accomplish the tasks of learning to integrate knowledge using integrated competencies as seen in adaptive experts, yet this understanding is key when considering how best to design curricula and instructional methods to help residents develop these skills. Therefore, the purpose of this study was to explore how residents develop these integrative skills in Pediatric medicine.\nA constructivist grounded theory study was conducted, using participant observation and semi-structured interviews as the data sources and purposeful sampling of residents from the Department of Pediatrics at the University of Toronto, to explore how residents develop integrative skills of adaptive expertise through workplace learning. We conducted 34 observations of ten residents, resulting in 102 hours of observation over the course of 12 months. Data collection and analysis occurred iteratively and themes were identified through constant comparative analysis by a team of researchers.\nOur results demonstrated that residents have acquired a number of routine efficiencies for communicating with patients and families during clinical consultations. While these well-developed approaches are effective in most clinical situations, residents navigated difficult or challenging conversations by enabling families to express their own narratives. They integrate this information with their medical knowledge and their own perspectives and values. At times, residents recognized that a ‘narrative shift’ was needed to effectively navigate the conversation. This shift was used purposefully to inform the creation of new communication strategies, resulting in an opportunity for new learning. Critically, this learning was modulated by the resident’s effectivities and the constraints of the clinical setting.\nNarrative shifts are adjustments in the clinician’s understanding of a patient’s narrative that impacts on how clinical care is provided. They are one representation of how integrated knowledge and competencies seen in adaptive experts are enacted in daily clinical work. In this study, narrative shifts prompted learning in residents. They triggered residents to explore and experiment with new ways of interacting with patients and families which further developed their conceptual understanding of how their knowledge is situated within the context. These narrative shifts also prompted them to seek multiple perspectives for approaching these conversations with families. The workplace learning environment provides opportunities that prepare residents for future learning through active experimentation, deeper conceptual learning and multiple perspectives. As we transition to competency based education, we must ensure that these key aspects of training that promote adaptive expertise development are not lost.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.349
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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