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Record W4389126357 · doi:10.1515/ijnes-2023-0059

E-learning modules to enhance student nurses’ perceptions of older people: a single group pre-post quasi-experimental study

2023· article· en· W4389126357 on OpenAlexafffund
Rashmi Devkota, Sherry Dahlke, Mary Fox, Sandra Davidson, Kathleen F. Hunter, Jeffrey I. Butler, Shovana Shrestha, Alison L. Chasteen, Elaine Moody, Lori Schindel Martin, Matthew Pietrosanu

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsToronto Metropolitan UniversityDalhousie UniversityUniversity of TorontoYork UniversityUniversity of CalgaryUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsLikert scalePerceptionPsychologyCognitionTest (biology)Knowledge levelNursingMedical educationMedicineMathematics educationDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine whether e-learning activities on cognitive impairment (CI), continence and mobility (CM) and understanding and communication (UC) improve student nurses' knowledge and attitudes in the care of older adults. METHODS: A quasi-experimental single group pre-post-test design was used. We included 299 undergraduate nursing students for the CI module, 304 for the CM module, and 313 for the UC module. We administered knowledge quizzes, Likert scales, and a feedback survey to measure student nurses' knowledge, ageist beliefs, and feedback on the modules respectively. RESULTS: Participants demonstrated significantly more knowledge and reduced ageist attitudes following the e-learning activities. CONCLUSIONS: Findings suggest that e-learning activities on cognitive impairment, continence and mobility, and understanding and communication improve knowledge and reduce ageist attitudes among nursing students.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.027
GPT teacher head0.442
Teacher spread0.415 · 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 designQualitative
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

Citations5
Published2023
Admission routes2
Has abstractyes

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