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Record W7110576108

Bridging competency and technology: User evaluation of a digital clinical workbook for nurse practitioner students

2025· article· en· W7110576108 on OpenAlexfundno aff

Bibliographic record

VenueUWA Profiles and Research Repository (UWA) · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State UniversityFlinders UniversityUniversity of TorontoUniversity of SurreyUniversity of WaterlooCurtin University of TechnologyUniversity of the Sunshine CoastUniversity of New South WalesAuckland University of Technology, New ZealandUniversity of WollongongUniversity of Waikato
KeywordsWorkbookBridging (networking)UsabilityDigital literacyEmployabilityLiteracyFocus group
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the impact of an educationally designed digital clinical workbook for Master of Nursing (Nurse Practitioner) students' work-integrated learning experience. Sixteen students completed an anonymous online survey consisting of closed questions and open-ended free text items to capture students' experience of the usability and utility of the digital clinical workbook to complete work-integrated assessments. Three focus groups were conducted comprising nine students and one alumnus. Despite initially finding the technology challenging, students found the digital platform navigable and useful. They preferred the digital clinical workbook over paper-based assessment methods and acknowledged the benefit of intentionally embedding the digital platform across the curriculum, the importance of providing user resources, and the influence of the workbook on employability skills. The digital clinical workbook is an innovative option for nurse practitioner students to complete clinical-based assessments while increasing their digital literacy and employability skills.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.069
GPT teacher head0.504
Teacher spread0.436 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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