MétaCan
Menu
Back to cohort
Record W3043828820 · doi:10.4018/ijmhci.2020040103

Developing Interactive Mobile Learning Experiences for Healthcare Professionals

2020· article· en· W3043828820 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Mobile Human Computer Interaction · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAsynchronous communicationContext (archaeology)Computer scienceKnowledge managementHealth careSituated learningResource (disambiguation)Collaborative learningHealth professionalsBest practiceMultimediaPsychologyPedagogy

Abstract

fetched live from OpenAlex

The purpose of this article was to examine best practices for designing inquiry-based contextual instructional content and determining the pedagogical uses and impacts of communities of practice for supporting mobile learning activities. In this convergent parallel mixed methods case study, mobile learning experiences were accessed by physicians, nurses, and healthcare professionals at medical organizations across Ontario. Impact was measured by the learning outcomes and experiences of study participants. Findings highlighted the effectiveness of context-specific, situated learning content for application of learned skills, integration of new knowledge, and identification of best practices. Synchronous discussion forums were examined for collaboration and communication during mobile learning, and asynchronous forums were ideal for post-learning collaboration, problem-solving and resource sharing.

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.

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)0.000
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: none
Teacher disagreement score0.686
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.399
Teacher spread0.353 · 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