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Record W4392498905 · doi:10.1016/j.teln.2024.02.009

Utilization and perception of a digital clinical tracking tool in undergraduate nursing education

2024· article· en· W4392498905 on OpenAlexafffund
Zahra Shajani, Catherine M. Laing, Amanda O’Rae, Justin Burkett, Kaleigh Peters

Bibliographic record

VenueTeaching and learning in nursing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsCurriculumTracking (education)Medical educationPerceptionNurse educationNursingReflection (computer programming)MedicineClinical PracticePsychologyComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Evaluating undergraduate nursing students’ clinical performance is a complex practice that relies on outdated methods that do not align with the growing use of technology in nursing curricula. This study is an exploration of nursing students’ and clinical instructors’ experiences using a digital evaluation tool or traditional paper-based evaluation tool. Third year nursing students and clinical instructors were given the opportunity to use a digital or paper-based evaluation tool during one semester; a survey was sent to all participants at the end of the semester. Student survey responses indicated that they preferred the digital evaluation tool as it provided for timely and specific in self-reflection and instructor feedback that was easily accessible. A digital evaluation tool is effective in supporting nursing students in the clinical evaluation process through encouraging reflection on specific learning behaviors, timely feedback from instructors, and an overall improvement in their clinical 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)0.003
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 score0.894
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.045
GPT teacher head0.436
Teacher spread0.392 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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