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Record W2298640847 · doi:10.5430/jnep.v6n8p1

Promoting empathy through immersive learning

2016· article· en· W2298640847 on OpenAlexvenueno aff
Debbie Roberts, Justine Mason, Emyr Williams, Nathan James Roberts, Rhiannon Macpherson

Bibliographic record

VenueJournal of Nursing Education and Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologyMental healthMental illnessQualitative researchApplied psychologyPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

Objectives: This paper reports on a mixed methods study to explore the use of immersive learning with a convenience sample of healthcare students (seven of Mental Health Nursing and twelve of Occupational therapy) in promoting empathy. Two immersive learning scenarios were created using real life stories of the symptoms experienced by people with psychosis and sufferers of Post Traumatic Stress Disorder (PTSD). Methods: Data were collected using a mixed methods approach: quantatively, using a pre and post test measure using two previously validated tools together with qualitative reflections related to the immersive learning experience. Results: The quantitative aspect of the study demonstrated that the immersive experience solidified the already positive attitude that the participants had towards mental health and to empathy. The qualitative findings demonstrate that immersive learning brought an awareness of being empathic to the fore. Conclusions: The findings provide evidence regarding the impact of immersive learning as a pedagogical approach. The experience provided students with an opportunity to embody people with mental illness, and students were able to consider their own future practice in relation to people experiencing auditory and visual hallucinations and flashbacks associated with PTSD.

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)0.008
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 score0.589
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.070
GPT teacher head0.440
Teacher spread0.370 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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