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Record W2071139970 · doi:10.5430/jnep.v5n7p30

Development of communication skills in healthcare: Perspectives of new graduates of undergraduate nursing education

2015· article· en· W2071139970 on OpenAlexvenueno aff
Lisa Kennedy Sheldon, Dany M. Hilaire

Bibliographic record

VenueJournal of Nursing Education and Practice · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)FeelingMedical educationCommunication skillsHealth careNursingPsychologyMedicineNurse education

Abstract

fetched live from OpenAlex

Background : Communication between healthcare providers and patients affects patient care and safety. Nurses develop communication skills at the undergraduate level, after graduation and throughout their practice. This study explored perspectives of new graduates on the development and implementation of communication skills within the first six months after graduation. Methods : Descriptive study using an electronic survey to three cohorts of recent graduates. Surveys were sent within six months after graduation. Results : A total of 700 surveys were sent to three cohorts of recent graduates with 206 surveys completed (response rates ranged from 26%-35%). Participants reported always feeling confident communicating with patients and families 27% of the time, and 23.5% of the time with interdisciplinary teams. Only 50.8% reported always feeling confident to provide safe care all of the time with 44.5% reported feeling always able to ask colleagues for help with challenging situations. Conclusions : Undergraduate nursing programs should incorporate more didactic communication skills training and simulation to prepare nursing students and increase their confidence to provide safe care and consult with colleagues for help. Communication skills training in practice settings after graduation to increase skill development and confidence during independent clinical practice.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.199
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.134
GPT teacher head0.550
Teacher spread0.416 · 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

Citations39
Published2015
Admission routes1
Has abstractyes

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