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Record W2184479711 · doi:10.34105/j.kmel.2015.07.026

Evaluation of an informatics educational intervention to enhance informatics competence among baccalaureate nursing students

2015· article· en· W2184479711 on OpenAlex
Manal Kleib, Kärin Olson

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueKnowledge Management & E-Learning An International Journal · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of Alberta
FundersFaculty of Nursing, University of AlbertaUniversity of Alberta
KeywordsInformaticsCompetence (human resources)Health informaticsMedical educationNursingIntervention (counseling)Health careTest (biology)MedicinePsychologyPublic healthEngineering

Abstract

fetched live from OpenAlex

Concerns around quality of care and patient safety have been key drivers behind the increased interest in improving informatics competencies among health care providers. The purpose of this study was to develop an informatics educational intervention for baccalaureate nursing students and compare outcomes associated with vodcasting and face-to-face methods for delivering this material. Following a pilot test, we used a three-group posttest only design to test the effect of the intervention on knowledge gain, confidence and attitude outcomes toward the electronic health record. Forty-two individuals participated in this study. Findings showed that the intervention had a large effect on knowledge gain (0.444), but no effect on confidence or attitudes, and that vodcasting was equally effective to face-to-face methods for delivering informatics content. Following refinement of the knowledge gain instrument used in this study, we urge replication of this study in other settings with a larger sample.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.000
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 head0.104
GPT teacher head0.489
Teacher spread0.385 · 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