Letter to the Editor: Critical need for effective communication skills education in nursing
Bibliographic record
Abstract
Wei (2018) [1], state that specific communication problems were linked to 17.5% of all complaints, but in reality other categories, totalling 83.7% of all complaints, have a clear (verbal and non-verbal) communication component; uncaring attitudes (26.7%), unsatisfactory quality of treatment and competence (26.5%) and processes of care (13.0%).Highlighting this point are the examples given by patients in relation to complaints categorised as uncaring attitudes, included "displayed indifferent expressions, used unfriendly language tones, were impatient with patients' questions, or did not give thorough explanations when answering questions".The authors suggest theory-guided strategies to assist with nursing care.We advocate the need for this to be instilled in nursing education.Education providers for health professionals aim to improve communication skills through formal education [2e4].Wei et al. (2018) [1] state the Chinese curriculum for medicine focuses on science more than humane or caring qualities.Few education providers are able to effectively educate health professionals of the critical nature of communication for their future practice and work place requirements [5].In the United States, poor communication contributed to 1744 deaths over a five-year period [6].The United Kingdom Nation Health Service (NHS) are acutely aware of the issues with poor communication, commissioning a document, Improving Communication with Patients in the NHS.The document states that good communication, not only improves health outcomes, but could save the UK one billion pounds annually, challenging the position that communication is a "soft skill" [7].Good clear communication in nursing, specifically demonstrated as the successful ability to "receive, decode information effectivity, exhibiting different types of communication in different nursing contexts through visual, auditory and kinaesthetic modes" [[8], p. 51 2014], is the foundation of a vibrant, safe and effective healthcare system.Thus, student nurses require an evidence based, consistent communication educational approach.How communication is taught affects health professional's work place, patient lives and has financial implications [2,9,10].Previous studies have demonstrated the inconsistency in communication education [11e13].Common educational methods currently used, include simulation, role-play, videos, debriefing and reflection [4,10e12,14,15].One method that has proven to be effective is
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".