Promoting patient rights in nursing care in Ghana through the Caring Space Model
Bibliographic record
Abstract
Objectives: To explore patient rights outcomes in nurse-patient clinical interactions in the Yendi Hospital and promote patients' rights in Ghana using a proposed Caring Space Model. Design: An ethnographic research design was implemented, and purposive sampling was used to recruit participants. Data were gathered across nine inpatient units through in-depth individual interviews (n = 39), ethnographic participant observations (over 400 hours), and a focus group discussion from December 2021 to April 2022. A reflexive thematic analysis was conducted to explore participants' knowledge, experiences, and barriers to upholding patient rights in clinical interactions. Setting: The study was conducted in the Yendi Municipal Hospital. Participants: Included Nurses (n=11), patients (n=21), and caregivers (n=11) who were 18 years of age or older and provided voluntary consent. Additionally, nurses must have at least three years of experience in a hospital setting. Results: failed to educate patients and caregivers because they feared that doing so would cause undue stress. Poor patient rights outcomes were rooted in human and material resource constraints, affecting nurse-patient and nurse-nurse manager relationships. Patient rights education, transformative nursing leadership practices, and effective communication during clinical interactions can enhance patient rights and safety. Conclusion: To promote patient-centred care, a model of the Caring Space has been developed to advance ethical nursing and caring practices that elevate patient rights in patient-provider clinical interactions. Funding: .
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".