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Record W1499814219 · doi:10.22230/jripe.2012v2n3a78

Developing a Virtual Interdisciplinary Research Community in Clinical Education: Enticing People to the “Tea-Room”

2012· article· en· W1499814219 on OpenAlexvenueno aff
Russell Butson, Paul Hendrick, Martin Kidd, M Brännström, Martin Hedberg

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupPerceptionDilemmaPsychologyMedical educationAffect (linguistics)Value (mathematics)Public relationsKnowledge managementMedicineComputer scienceMarketingBusinessPolitical science

Abstract

fetched live from OpenAlex

Background: Many interdisciplinary collaborative research programs in the health sector are adopting the community of practice concept within virtual environments. This study explores the factors that affect the members of a geographically dispersed group of health professionals in their attempt to create an interprofessional Virtual Community of Practice (VCoP) from which to promote clinical education research.Method & Findings: A survey was used to determine participants’ degree of computer competency. System logs recorded members’ access details and site activity. Member perceptions and beliefs were established using focus groups. While members stated they were enthusiastic about the VCoP, the primary use was viewing. Their online behaviour indicated that on average it took six visits to generate a post. This suggests a stronger focus on viewing (consumption of) information than on contributing (construction of) information.Conclusions: We believe it is crucial for members to contribute during the initial phase of any pre-structured VCoP in order to overcome the consumption-construction dilemma. It is during this initial phase that members will decide on the community’s value. If the community cannot offer added value, members who engage are likely to consume for a time and then leave.

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)0.154
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1540.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0000.005
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 head0.407
GPT teacher head0.672
Teacher spread0.264 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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