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Record W4320500776 · doi:10.26463/rjns.13_1_4

Effect of Communication Skills Training on Empathy of Nursing Students

2023· article· en· W4320500776 on OpenAlexaboutno aff
Patsey Sera Castelino, Theresa Leonilda Mendonca

Bibliographic record

VenueRGUHS Journal of Nursing Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologyCurriculumTest (biology)NursingQuality (philosophy)Presentation (obstetrics)Medical educationClinical psychologyMedicineSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

Background Empathy is a necessary factor in the provision of quality nursing care. Empathy is a skill that may be acquired. The authors aimed to evaluate the change in empathy levels in nursing students following communication skills training. Methods An evaluative pre-experimental one group pretest posttest design was used to collect data from hundred first year undergraduate nursing students. Toronto Empathy Questionnaire was used to determine the empathy level at baseline and after the intervention. Communication skill training was provided in the form of PowerPoint presentation role paly various exercises group activities demonstration and discussion.Results The mean empathy scores of students at posttest 45.03plusmn6.39 were higher than the mean empathy levels at pretest 42.6plusmn6.4. There was a significant difference in the post test empathy scores t4.6 p0.00001. The study showed significant improvement immediately in empathy levels following communication skills training.Conclusion The findings suggest a need to incorporate a regular training program into the existing nursing curriculum to enhance empathy and prevent its decline over the years.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.067
GPT teacher head0.466
Teacher spread0.399 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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