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Record W4220696455 · doi:10.1016/j.pec.2022.03.008

Communication skills training for nurses: Is it time for a standardised nursing model?

2022· article· en· W4220696455 on OpenAlexaff
Debra Kerr, Peter Martin, Lynn Furber, Sandra Winterburn, Sharyn Milnes, Annegrethe Nielsen, Patricia H. Strachan

Bibliographic record

VenuePatient Education and Counseling · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNursingCommunication skillsCommunication skills trainingPatient safetyTraining (meteorology)Nurse educationMedicineProcess (computing)PsychologyMedical educationHealth careComputer science

Abstract

fetched live from OpenAlex

Communication is a core and complex skill required by all healthcare professionals (HCP), an activity that is affected by attitudes, emotions and knowledge [1].The process of communication is central to effective, safe, patient-centred and compassionate nursing practice.Communication between nurses, patients and their families can be improved with education and training [2].We contend that not only can it be improved, it must be improved, and that education is key.The stakes for nurses' communication are high as its' effectiveness impacts patient safety and sentinel events, workplace culture and job satisfaction [3].Globally, most patient complaints relate to communication breakdowns with HCPs [4].For communication in nursing education to advance globally in strategic ways, an understanding of the current educational landscape is critical.The aim of this paper is to

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)0.021
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0080.010
Open science0.0030.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0050.002

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 head0.138
GPT teacher head0.450
Teacher spread0.312 · 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 designNot applicable
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

Citations47
Published2022
Admission routes1
Has abstractyes

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