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Teaching and Learning in a Laptop Nursing Program

2007· book-chapter· en· W2497337452 on OpenAlexaffabout
Ellen Vogel, Bill Muirhead

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPreparednessInformation and Communications TechnologyLaptopMedical educationHealth careNursingPsychologyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Increasingly, nurses work in practice settings that employ the latest information and communication technology (ICT) to research, administer, and deliver healthcare to clients. Thus, it is critical that BNSc program graduates be competent with the technology that is embedded in their nursing environments. This chapter explicates the findings of a study designed to assess and prioritize the capacities of nursing faculty in the use of ICT for teaching and learning. Data was gathered over a two-year period through in-depth interviews, questionnaires, learning journals, and document review and synthesis. The authors hope that findings will contribute to the development of core competencies in the use of ICT for teaching and learning. Further, outcomes will inform decision-makers and funding agencies of the needs and gaps related to faculty ICT preparedness in Canadian schools of nursing. Recommendations address key success factors including faculty development and institutional support.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.038
GPT teacher head0.341
Teacher spread0.304 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2007
Admission routes2
Has abstractyes

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