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Record W7046961084

The Effects of an Emotional Intelligence and Empathy Interactive Education Program with Prelicensure Nursing Students

2024· article· en· W7046961084 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Crossing (Liberty University) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyEmotional intelligenceWilcoxon signed-rank testTest (biology)Nurse educationMoodParticipant observation
DOInot available

Abstract

fetched live from OpenAlex

A dissertation is presented on a teaching program for emotional intelligence (EI) and empathy with a cohort of nursing students enrolled in a traditional, undergraduate nursing college in the Northeast region of the United States (US). An interactive teaching program was used with various media to present new material and learning activities during the spring semester. The importance of teaching nursing students the aspects of EI was to prepare them for the rigors of the profession by allowing them to delve into their own self-awareness and emotional regulation, which in turn helps to connect with patients in a more robust and empathetic manner. This quasi-experimental study had 36 participants complete the Trait-Meta Mood Scale-24 (TMMS-24) and 33 for the Toronto Empathy Questionnaire (TEQ) at two set points in the semester. A paired-sample t-test was completed, along with the Shapiro-Wilk and Wilcoxon Signed Rank Test to determine a normal distribution. The results indicated data that was not statistically significant, and the researcher failed to reject the null hypotheses. However, mean scores in females improved in both the TMMS-24 and TEQ, as did Hispanic/Latino and Asian participant scores in the TMMS-24, as well as one participant who had prior topic training. Future research is recommended on this subject considering correlation studies, nursing program type, increased participant numbers, race, and combining different instruments.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.295
Teacher spread0.288 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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