MétaCan
Menu
Back to cohort
Record W1605427656 · doi:10.19173/irrodl.v13i3.1221

Using self-efficacy to assess the readiness of nursing educators and students for mobile learning

2012· article· en· W1605427656 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2012
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversity of OttawaNorth Island CollegeAthabasca University
Fundersnot available
KeywordsSelf-efficacyMedical educationMobile deviceMobile technologyScale (ratio)PsychologyEducational technologySurvey instrumentNurse educatorTechnology integrationM-learningNurse educationNursingMedicineMathematics educationComputer scienceApplied psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the self-efficacy of nursing faculty and students related to their potential use of mobile technology and to ask what implications this technology has for their teaching and learning in practice education contexts. We used a cross-sectional survey design involving students and faculty in two nursing education programs in a western Canadian college. In January, 2011, 121 faculty members and students completed the survey. Results showed a high level of ownership and use of mobile devices among our respondents. The median mobile self-efficacy score was 75 on a scale of 100, indicating that both faculty and students were highly confident in their use of mobile technologies and prepared to engage in mobile learning.<br /><br />

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
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 science0.0020.001
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 head0.189
GPT teacher head0.539
Teacher spread0.350 · 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