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Record W4200485863 · doi:10.5430/jnep.v12n5p8

Blended learning in a health assessment course: A mixed-methods study

2021· article· en· W4200485863 on OpenAlexvenueno aff
Sara Hallowell, Tomeka Dowling

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationBlended learningFace-to-facePsychologyPerceptionCourse (navigation)Qualitative researchQualitative propertyNursingMedicineMathematics educationComputer scienceEducational technologyEngineeringSociology

Abstract

fetched live from OpenAlex

Objective: The purpose of this study was to get students’ perceptions about changes made to the health assessment course delivery format from face to face to blended learning (BL). Health assessment is a foundational course in nursing undergraduate programs. Research has suggested that students have high levels of satisfaction with a blended learning format.Methods: A survey was used to gather students’ perceptions about changing a health assessment course from face-to-face delivery format to a blended learning format. All second year BSN students who were registered for the course (N = 88) were invited to participate in the survey at the end of the semester.Results: Most students in this study preferred face to face course delivery. Qualitative results were grouped together into themes: 1) Engagement, 2) E-learning tool, and 3) Confidence. Opinions were mixed concerning the e-learning materials that were used. Overall, students felt they were confident in their assessment skills as they prepared to enter the clinical environment.Conclusions: Findings from this study will impact methods of teaching health assessment and other nursing courses in the future.

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)0.013
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.792
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.206
GPT teacher head0.657
Teacher spread0.451 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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