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Record W4296522719 · doi:10.1111/jocn.16542

Behavioural indicators of compassionate nursing care of individuals with complex needs: A naturalistic inquiry

2022· article· en· W4296522719 on OpenAlexaffabout
Ahtisham Younas, Caroline Porr, Joy Maddigan, Julia Moore, Pablo Navarro, Dean Whitehead

Bibliographic record

VenueJournal of Clinical Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsImperial College of TorontoNewfoundland and Labrador Centre for Applied Health ResearchMemorial University of Newfoundland
FundersScience for Equity, Empowerment and Development Division
KeywordsNursingThematic analysisCompassionEmpathyHealth carePsychologyMental healthAltruism (biology)MedicineActive listeningPopulationQualitative researchSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

AIMS AND OBJECTIVES: To explore behavioural indicators of compassionate nursing care from the perspectives of individuals with multimorbidities and complex needs. BACKGROUND: Complex patients are individuals with multimorbidity and/or mental health concerns, andoften with medication and drug-related problems requiring ongoing person-centered care, mental health interventions, and family and community resources. They are frequent consumers of health-care services and it is documented that these patients experience discrimination and substandard care. Compassionate care can improve patient care experiences and health outcomes. However, missing is the guidance on how to provide compassionate care for this population from the perspectives of complex patients. DESIGN: A qualitative descriptive approach was conducted in eastern Canada from December 2020-April 2021. The COREQ guidelines were followed for reporting. METHODS: Data from in-person and virtual semi-structured interviews with 23 individuals having experiences as complex patients were analysed using reflexive thematic analysis. Among them 19 were homeless and lived in a shelter. FINDINGS: Six indicators of compassionate nursing care were generated: sensitivity, awareness, a non-judgmental approach, a positive demeanour, empathic understanding, and altruism. CONCLUSIONS: Individuals perceived that nurses who acknowledge personal biases are better at providing compassionate care by manifesting compassion through their genuine and selfless interest in the complicated health problems and underlying socio-cultural determinants of each patient. Kindness, positivity, and a respectful nursing approach elicit openness and the sharing of heartfelt concerns. RELEVANCE TO CLINICAL PRACTICE: Comprehensive health assessment, dedicated efforts to know the patient as a human being, and listening to the patient's preferences can improve health outcomes among individuals with complex needs. Healthcare administrators can effect the change by supporting nurses to address complex health and social care needs with compassion. PATIENT OR PUBLIC CONTRIBUTION: Patients and healthcare professionals helped in data collection at the community care centre.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.120
GPT teacher head0.453
Teacher spread0.333 · 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 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

Citations15
Published2022
Admission routes2
Has abstractyes

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