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Record W1990727820 · doi:10.1080/01421590600922909

The transition from face-to-face to online CME facilitation

2006· article· en· W1990727820 on OpenAlexaff
Jocelyn Lockyer, Joan Sargeant, Vernon Curran, Lisa Fleet

Bibliographic record

VenueMedical Teacher · 2006
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMemorial University of NewfoundlandDalhousie UniversityUniversity of Calgary
Fundersnot available
KeywordsNoveltyFacilitationFace-to-facePsychologyPhoneOnline courseMathematics educationMedical educationPedagogyMultimediaComputer scienceSocial psychologyMedicine

Abstract

fetched live from OpenAlex

This study examines the experiences of nine medical teachers who transitioned from face-to-face teaching to facilitating a course in an online environment. The authors examined the reasons why the teachers agreed to facilitate an online course, the challenges they encountered and their practical solutions, and the advantages and disadvantages they perceived to this teaching environment. Thirty-minute phone interviews were conducted. An iterative process was used to develop the themes and sub-themes for coding. Teachers reported being attracted to the novelty of the new instructional format and saw online learning as an opportunity to reach different learners. They described two facets to the transition associated with the technical and facilitation aspects of online facilitation. They had to adapt their usual teaching materials and determine how they could make the 'classroom' user friendly. They had to determine ways to encourage interaction and facilitate learning. Lack of participation was frustrating for most. This study has implications for those intending to develop online courses. Teacher selection is important as teachers must invest time in course development and teaching and encourage participation. Teacher support is critical for course design, site navigation and mentoring to ensure teachers facilitate online discussion.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.333
Teacher spread0.315 · 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.

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

Citations35
Published2006
Admission routes1
Has abstractyes

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