Implementation strategies to promote compassionate nursing care of complex patients: An exploratory sequential mixed methods study
Bibliographic record
Abstract
INTRODUCTION: Individuals with multiple physical and, or, mental health issues and, or, drug-related problems are known as complex patients. These patients are often recipients of poor-quality care. Compassionate nursing care is valuable to promote better care experiences among this patient population. Implementation strategies should be designed to enhance compassionate nursing care delivery. The study aimed to gain understanding of barriers to compassionate care delivery to propose implementation to promote compassionate nursing care of complex patients. DESIGN: An exploratory sequential mixed methods study was conducted. METHODS: Phase 1 was the qualitative component during which 23 individuals with multimorbidities were interviewed for exploring their perceptions of barriers to compassionate nursing care. The barriers were integrated with implementation science frameworks using the building technique during phase 2 to develop a Q-sort survey of implementation strategies for phase 3. Nurses, nurse managers, health care administrators, policymakers, and compassionate care experts responded to the survey by ranking the 21 implementation strategies, out of which five met the Q-factor analysis criteria. RESULTS: Participant-perceived barriers to nurse compassion could be categorized under knowledge, intentions, skills, social influences, behavioral regulation, reinforcement, emotion, and environmental context and resources. The five highest-ranked strategies included facilitation, consultation with stress experts, involvement of patients and families, modeling compassion through shadowing, and utilizing implementation teams. CONCLUSIONS: Enablement and modeling were the integration functions represented by the highest-ranked implementation strategies. Enabling nurses to provide compassionate care through emotional support and mental health counseling, and, modeling compassion and compassionate care through shadowing were recommended and rated as highly relevant by the majority of stakeholders. CLINICAL RELEVANCE: Enhancing nurses compassionate behaviors toward complex patients requires facilitating them in enacting compassion in practice through modeling and support from organizations and nurse managers.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".