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Record W3197926488 · doi:10.1186/s12912-021-00684-2

A literature-based study of patient-centered care and communication in nurse-patient interactions: barriers, facilitators, and the way forward

2021· review· en· W3197926488 on OpenAlexaff
Abukari Kwame, Pammla Petrucka

Bibliographic record

VenueBMC Nursing · 2021
Typereview
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
Fundersnot available
KeywordsNursingHealth careMedicineNursing researchPerceptionQuality (philosophy)Nursing carePsychology

Abstract

fetched live from OpenAlex

Providing healthcare services that respect and meet patients' and caregivers' needs are essential in promoting positive care outcomes and perceptions of quality of care, thereby fulfilling a significant aspect of patient-centered care requirement. Effective communication between patients and healthcare providers is crucial for the provision of patient care and recovery. Hence, patient-centered communication is fundamental to ensuring optimal health outcomes, reflecting long-held nursing values that care must be individualized and responsive to patient health concerns, beliefs, and contextual variables. Achieving patient-centered care and communication in nurse-patient clinical interactions is complex as there are always institutional, communication, environmental, and personal/behavioural related barriers. To promote patient-centered care, healthcare professionals must identify these barriers and facitators of both patient-centered care and communication, given their interconnections in clinical interactions. A person-centered care and communication continuum (PC4 Model) is thus proposed to orient healthcare professionals to care practices, discourse contexts, and communication contents and forms that can enhance or impede the acheivement of patient-centered care in 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 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)0.007
metaresearch head score (Gemma)0.023
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: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.012
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.137
GPT teacher head0.445
Teacher spread0.307 · 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
GenreReview

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

Citations988
Published2021
Admission routes1
Has abstractyes

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