MétaCan
Menu
← Back to cohort
Record W2504741310 · doi:10.3233/978-1-61499-658-3-981

Diffusing Innovations in Nursing Education: From PDAs to OERs

2016· article· en· W2504741310 on OpenAlexaffabout
Glynda Doyle, Bernice Budz

Bibliographic record

VenueStudies in health technology and informatics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsDiffusion of innovationsKnowledge managementComputer scienceEngineering ethicsSociologyEngineeringSocial science

Abstract

fetched live from OpenAlex

The Canadian educational and healthcare practice landscapes are ever-evolving technologically. In response to these changes, the British Columbia Institute of Technology (BCIT) has integrated various educational technologies using Rogers Diffusion of Innovation model as a guiding framework for this integration with considerable success. This poster describes BCIT's journey with diffusing innovations, discusses examples of these technological integrations in accordance with Roger's model, and outlines several implications for educational 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)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0060.014
Scholarly communication0.0180.016
Open science0.0020.014
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.173
GPT teacher head0.571
Teacher spread0.397 · 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 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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueStudies in health technology and informatics→Same topicHealth Sciences Research and Education→French-language works237,207→