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Record W2050135178 · doi:10.1111/medu.12376

Progressive collaborative refinement on teams: implications for communication practices

2014· article· en· W2050135178 on OpenAlexaff
Mark Goldszmidt, Tim Dornan, Lorelei Lingard

Bibliographic record

VenueMedical Education · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Medical teaching teams (MTTs) must balance teaching and patient care in the face of three challenges: shifting team membership, varying levels of learners and patient complexity. To support care, MTTs rely on a combination of recurrent oral and written communication practices (genres), such as admission, progress and discharge notes. The purpose of this study was to explore how these genres influence the team's ability to collectively care for patients. METHODS: This was a multiple case study with data collected through observations and audio-recordings of 19 patient cases focusing on admission review discussions and chart documents throughout the hospitalisation. Participants included 14 medical students, 32 residents and 10 attending physicians rotating through one of three internal medicine MTTs. We used constant comparative analysis to identify recurrent patterns across the multiple cases, which were further elaborated in a return-of-findings focus group. RESULTS: The MTT genre system facilitated the care of patients through 'progressive collaborative refinement' (PCR): MTTs use case and data reviews to collaboratively and progressively refine their understanding of the patient's problems and develop strategies for addressing them. Progressive collaborative refinement was apparent through modifications made in the documentation. Although modifications were a necessary component, they were not sufficient: some modifications were made without refinement. We characterised incidents of failed modification as 'fragmentation'. Three types were observed: conceptualisation, documentation and continuity of care providers. In most cases, all three were present and interacted to impede PCR. CONCLUSIONS: Progressive collaborative refinement was used by MTTs to provide the optimal care to patients. Progressive collaborative refinement was impeded by a lack of continuity of care providers and gaps between communication genres that fragmented conceptualisation and documentation. Progressive collaborative refinement can be understood as both an overarching process and a shared but unstated ideal. Through defining and describing PCR, the present findings can be used to improve communication and teaching.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.697
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.443
Teacher spread0.423 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations28
Published2014
Admission routes1
Has abstractyes

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