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Record W2941270280 · doi:10.1080/17533015.2019.1608569

The arts as a catalyst for learning with undergraduate nursing students: findings from a constructivist grounded theory study

2019· article· en· W2941270280 on OpenAlexafffund
Kendra L. Rieger, Wanda M. Chernomas, Diana E. McMillan, Francine Morin

Bibliographic record

VenueArts & Health · 2019
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsHealth Sciences CentreUniversity of Manitoba
FundersCanadian Institutes of Health ResearchUniversity of ManitobaManitoba Health Research Council
KeywordsTransformative learningConstructivist grounded theoryGrounded theoryNurse educationConstructivist teaching methodsThe artsPsychologyPedagogyNursing theoryNursingMedical educationTeaching methodQualitative researchMedicineSociologyMEDLINEVisual arts

Abstract

fetched live from OpenAlex

Background: There is a growing interest in arts-based pedagogy (ABP) to promote the wide range of competencies needed for professional nursing. The aim of this study was to develop a theoretical understanding of how students learn through ABP in undergraduate nursing education.Methodology and Methods: We used a constructivist grounded theory methodology which incorporated art-elicitation interviews. Thirty nursing students and eight nurse educators shared about their ABP experiences. Data were analyzed with grounded theory procedures.Results: The arts as a catalyst for learning emerged as the core category and elucidates how the unique quality of the arts created powerful pedagogical processes for many students. When students engaged with these processes, they resulted in surprising and transformative learning outcomes for professional nursing.Conclusions: These findings provide insight into why and how students learned through ABP, and can inform the effective implementation of ABP into healthcare education.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.041
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0080.016
Scholarly communication0.0090.004
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.361
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2019
Admission routes2
Has abstractyes

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