Effective nursing leadership as a catalyst for person-centered care and positive nursing-patient interactions: evidence from a public Ghanaian hospital
Bibliographic record
Abstract
Abstract Person-centered care (PCC) is crucial to patient engagement in healthcare enhancing patients’ participation in critical care decision-making, increasing care disclosure, reducing medication errors, and promoting satisfaction with care outcomes. Healthcare management and leadership practices contribute to effective communication and interactions between healthcare providers and patients, which is vital for quality PCC outcomes and patient perceptions of care providers. However, little is known about how nursing leadership influences PCC and clinical interactions in the Ghanaian setting, which this study saw as a gap and aims to fill. This paper reports data from interdisciplinary exploratory qualitative research to examine the impacts of nursing leadership practices on nurse-patient relationships and care outcomes. Nurses (11), patients (22), and caregivers (11) participated in the study. Data were gathered in Ghana through interviews, focus groups, and participant observations and analyzed thematically. The three themes which emerged were: hospital leadership and the nursing staff, healthcare management practices, and communication barriers regarding how nursing leadership impacts PCC. Poor relationships between nurses and hospital leaders affected nurses’ caring practices. Management practices, including an annual rotation of nurses across different patient wards and exigent patient record management routines, negatively impacted care delivery and patient-provider interactions. These leadership practices and the strained relationships between nurses and hospital leaders potentially derail effective PCC. Nursing and hospital managers must embrace transformational leadership and healthcare management practices, especially in resource-scare settings, that foster a trusting care culture and/or environment for therapeutic nurse-patient relationships to thrive and for PCC to be actualized.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".