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Record W3185894096 · doi:10.1515/ijnes-2021-0033

Testing and e-learning activity designed to enhance student nurses understanding of continence and mobility

2021· article· en· W3185894096 on OpenAlexaff
Sherry Dahlke, Kathleen F. Hunter, Matthew Pietrosanu, Maya R. Kalogirou

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2021
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLikert scaleTest (biology)PerceptionPsychologyKnowledge levelMedicineMedical educationNursingMathematics education

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to test if the e-learning activity that we developed could improve student nurses' knowledge of continence and mobility and whether or not students would find the style of learning beneficial. METHODS: A quasi-experimental pre-post-test design was used to test if the continence and mobility e-learning activity could improve student nurses' knowledge about assessing and managing the needs of continence and mobility. An 18-item true/false knowledge of continence quiz was completed by 116 student nurses and a Likert style feedback learning survey was completed by 135 nursing students. RESULTS: There was a statistically significant increase in students' knowledge about continence and its relationship to mobility following the e-learning activity. The e-learning activity also enhanced students' knowledge, confidence and perceptions about older people. CONCLUSIONS: The e-learning activity we developed has the potential to improve nursing students' knowledge about continence and mobility in an enjoyable manner.

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)0.005
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 score0.076
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.135
GPT teacher head0.497
Teacher spread0.362 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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