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Record W3158366127 · doi:10.1108/heed-10-2020-0037

A community of practice for graduate students in health sciences

2021· article· en· W3158366127 on OpenAlexafffund
Liquaa Wazni, Wendy Gifford, Christina Cantin, Barbara Davies

Bibliographic record

VenueHigher Education Evaluation and Development · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsQueen's UniversityUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsCommunity of practiceMedical educationOriginalityPsychologyQualitative researchContext (archaeology)PedagogyMedicineSociology

Abstract

fetched live from OpenAlex

Purpose The aim of this study was to describe the experiences of graduate students who participated in the community of practice (CoP) and identify areas for improvement to support academic success. Design/methodology/approach In total, 19 graduate students engaged in a CoP to facilitate social interactions, knowledge sharing and learning within a culture of scholarship. A descriptive qualitative research study was conducted using semistructured interviews with eight participants who had attended the CoP meeting. Findings All participants were from the School of Nursing and perceived the CoP to be beneficial, particularly international students who had challenges in adapting to new academic and social environments. Areas for improvement include creating a group structure that enhances belonging and learning. Originality/value This is the first CoP that was implemented at the Faculty of Health Sciences at the authors’ university. It has been the authors’ experience that a CoP can benefit graduate students through networking, knowledge sharing, social support and learning. The finding of this research will be used to inform a new CoP to address the needs of graduate students. The authors will be adapting the CoP to the current context that includes a virtual platform during the COVID-19 pandemic and will include content specific for international students.

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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.306
GPT teacher head0.623
Teacher spread0.317 · 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 designObservational
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

Citations3
Published2021
Admission routes2
Has abstractyes

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