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Record W2566655918 · doi:10.3912/ojin.vol21no02man05

Information and Communication Technology: Design, Delivery, and Outcomes from a Nursing Informatics Boot Camp

2016· article· en· W2566655918 on OpenAlexaboutno aff
Manal Kleib, Nicole B. Simpson, Beverly Rhodes

Bibliographic record

VenueOJIN The Online Journal of Issues in Nursing · 2016
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordsInformaticsHealth informaticsBoot campInformation and Communications TechnologyHealth Administration InformaticsNursingInformation technologyWork (physics)Knowledge managementHealth careMedical educationMedicineComputer scienceEngineeringPolitical scienceWorld Wide WebPublic health

Abstract

fetched live from OpenAlex

Information and communication technology (ICT) is integral in today’s healthcare as a critical piece of support to both track and improve patient and organizational outcomes. Facilitating nurses’ informatics competency development through continuing education is paramount to enhance their readiness to practice safely and accurately in technologically enabled work environments. In this article, we briefly describe progress in nursing informatics (NI) and share a project exemplar that describes our experience in the design, implementation, and evaluation of a NI educational event, a one-day boot camp format that was used to provide foundational knowledge in NI targeted primarily at frontline nurses in Alberta, Canada. We also discuss the project outcomes, including lessons learned and future implications. Overall, the boot camp was successful to raise nurses’ awareness about the importance of informatics in nursing 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 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)0.002
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.430
Teacher spread0.393 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations17
Published2016
Admission routes1
Has abstractyes

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