Behavioural indicators of compassionate nursing care of individuals with complex needs: A naturalistic inquiry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".