Nurses’ Challenges to Developing Interpersonal Relationships During Integrated Care for Complex Patients
Bibliographic record
Abstract
BACKGROUND: Individuals with multimorbidity and complexity have multifaceted care needs requiring integrated and collaborative care from nurses, families, and health care teams. Nurses, as the frontline care professionals, should develop therapeutic relationships with patients and their families and professional relationships with health care team members to ensure the delivery of effective integrated care. Failure to develop effective interpersonal and professional relationships can negatively affect patient care. OBJECTIVE: The purpose of this study was to explore nurses' challenges with developing interpersonal and professional relationships during integrated care for individuals with multimorbidity and complexity. METHODS: A descriptive qualitative design was used. We interviewed a purposive sample of 19 nurses with experience of caring for individuals with multimorbidity and complexity across two hospitals in Pakistan. Semi-structured interviews were used for data collection, and data were analyzed using reflexive thematic analysis. RESULTS: Two challenges were identified affecting the relationships between patients' families and nurses, and two challenges influencing the professional relationships within the team. Families withheld information, controlled care access of their relatives, posed unrealistic demands, and abused nurses, affecting nurse-family relationships. Power struggles to demonstrate authority in decision-making were common within health care teams, affecting nurses' professional capacity to provide effective care. CONCLUSIONS: Health care team, patient, and family collaboration is instrumental in improved care for individuals with multimorbidity and complexity. Nurse leaders and health care organizations should take initiatives to address nurses' interpersonal confrontations to support them in the provision of quality care.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".