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Record W4385987671 · doi:10.4018/ijthi.328578

The Virtual Community of Practice Facilitation Model

2023· article· en· W4385987671 on OpenAlexaffabout
Hugh Kellam, Clare Cook, Deborah L. Smith, Pam Haight

Bibliographic record

VenueInternational Journal of Technology and Human Interaction · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsNOSM UniversityUniversity of Ottawa
Fundersnot available
KeywordsAsynchronous communicationMedical educationAsynchronous learningComputer scienceFacilitationKnowledge managementPsychologyMedicinePedagogyTeaching methodCooperative learningSynchronous learning

Abstract

fetched live from OpenAlex

This study examines the instructional design, learning experiences, and outcomes of a virtual community of practice (VCoP). In 2019, the Northern Ontario School of Medicine launched a continuing professional development program consisting of an asynchronous online module followed by an optional series of facilitated case-based videoconference workshops, designed as a VCoP. This program evaluation study employed a convergent parallel mixed methods design and combined data sources from participant pre- and post-program surveys and reflections with a content analysis of semi-structured interviews. The paper reports key enablers that contributed to the following outcomes: the value of an online module as a baseline of knowledge; the impact of the shared case studies, experiences, and peer support on reflection and modifications to medical practice; and skill development and patient-centered care as a result of module and VCoP participation. A model for the effective design and delivery of VcoPs is proposed that results in acquisition of new knowledge and skills and promotes patient-centred practice.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.004
Open science0.0020.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.055
GPT teacher head0.508
Teacher spread0.453 · 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 designTheoretical or conceptual
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

Citations4
Published2023
Admission routes2
Has abstractyes

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