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Record W62816202

Knowledge Creation at Multidisciplinary Patient Care Meetings: Implications for the Use of Collaborative Information Technology

2006· article· en· W62816202 on OpenAlexaboutno aff
Vanessa Vogwill

Bibliographic record

VenueJournal of the Association for Information Systems · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMultidisciplinary approachKnowledge managementContext (archaeology)Health careWork (physics)Knowledge sharingMedical educationComputer scienceMedicineEngineeringSociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper describes a study of Bullet Rounds in General Internal Medicine at an urban teaching hospital in Toronto, Canada. Bullet Rounds are multidisciplinary meetings of healthcare professionals involved in patient care. The goal of the study was to examine the processes of the meetings to understand the level of collaboration and knowledge exchange that takes place, the issues faced by such groups, and the potential role of information technology in supporting them. The methodology included data collection through observation, and quantitative analysis of verbal exchanges in Bullet Rounds. Using the Knowledge Management framework, options for support using information technology are discussed. The author concludes that Knowledge portals that can be used for repositories, prompts and sharing may be helpful in the context of Bullet Rounds. The study extends previous work on analysis of verbal exchanges and contributes to knowledge of collaborative practices in multidisciplinary groups meetings of healthcare practitioners.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2006
Admission routes1
Has abstractyes

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