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Record W7067649981

Mobile Self Efficacy in Canadian Nursing Education Programs

2010· other· en· W7067649981 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

VenueAUSpace (Athabasca University) · 2010
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNurse educationSelf-efficacyMobile deviceMobile technologyContinuing education
DOInot available

Abstract

fetched live from OpenAlex

The paper was given in the allotted time (15 minutes) and there was time for about 2 questions. No attendee discussed the paper with us afterwards, which was a bit disappointing, but likely a result of the conference organizers bundling together a series of papers on disparate topics. Our paper, however, was published in the conference proceedings: 
\n
\nKenny, R.F., Park, C.L., Van Neste-Kenny, J.M.C., & Burton, P.A. (2010). Mobile Self-Efficacy in Canadian Nursing Education Programs. In M. Montebello, V. Camilleri and A. Dingli (Eds.), Proceedings of mLearn 2010, the 9th World Conference on Mobile Learning, Valletta, Malta. 
\n
\nWe are now considering submitting an enhanced version to a special conference edition of the International Journal of Mobile and Blended Learning (IJMBL), which is a partner journal to IAMLearn, which put on the conference.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.534
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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