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Record W2098893136 · doi:10.4018/ijopcd.2012100105

Online Interest Groups

2012· article· en· W2098893136 on OpenAlexaff
Beverley Getzlaf, Sherri Melrose, Sharon Moore, Helen Ewing, James Fedorchuk, Tammy Troute-Wood

Bibliographic record

VenueInternational Journal of Online Pedagogy and Course Design · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsLikert scaleMedical educationGraduate studentsPsychologyDescriptive statisticsComputer-assisted web interviewingExploratory researchPerceptionData collectionSpace (punctuation)MedicineComputer scienceSociology

Abstract

fetched live from OpenAlex

This article discusses a 15 month educational innovation project, the objective of which was to investigate the perceptions of health profession students about their participation in a program-wide virtual community gathering space (Clinical Interest Groups) during their online graduate studies. Participants were students in two graduate programs who joined online forum discussions of the Clinical Interest Groups. The project was developed as action research and employed an exploratory, descriptive methodology to generate data from three sources: participant responses to a 15-item Likert type questionnaire, five open-ended questions included on the questionnaire, and online postings contributed by participants to the forum discussions. Findings of use to online educators are that the Clinical Interest Groups provided a gathering place in which graduate students could discuss common interests and support one another, and that participation in the groups was limited due to competing demands on students’ time from other commitments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.087
GPT teacher head0.432
Teacher spread0.345 · 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 designOther design
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

Citations7
Published2012
Admission routes1
Has abstractyes

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