Mapping the experiences of patient centered care for nurses in the role of patients: A scoping review
Bibliographic record
Abstract
Background. Patient- or person-centred care (PCC) has become a core competency that healthcare professionals must develop to deliver high-quality care. PCC enhances positive patient perceptions of care, promoting patient safety and overall health outcomes. Yet, little is known about PCC from nurses who become patients. Aim. To map nurse patients’ experiences and perceptions of PCC and how their illness experiences influence nursing practice after recovery. Methods. A scoping review methodology was implemented. Scopus, PubMed, Ovid Medline, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched for relevant literature using a comprehensive list of keywords, including nurse patient, nurse-as-patient, person-centred care, person-focused care, hospital, experiences, etc. Thematic analysis was conducted on extracted data, and the results were reported narratively. Results. Thirty-seven (37) studies were included in this review. Four broad themes: experiences of PCC, care practices that promote PCC, the influence of illness experience on nursing practices, and nurse patients’ unique contextual issues were developed, which revealed that nurse patients experienced heightened fear, anxiety, denial, uncertainty, and discomfort when diagnosed with life-threatening illnesses due to their knowledge of the healthcare system. While navigating treatment, most nurse patients experience care that aligns with PCC dimensions (respecting patient dignity, treating patients as persons, providing adequate information, and effective communication). Others received care that deviated from PCC principles, including waiting for long periods to access treatment services, being stereotyped, not being provided enough information, and being less engaged in their care. Furthermore, nurse patients faced unique challenges, including role ambiguity and confidentiality and privacy concerns. Nonetheless, they promoted PCC in their nursing practice after recovery by advocating for patients, serving on healthcare boards, influencing policy change, and becoming peer educators. Conclusion. Research from different nurse patients is needed to deepen our understanding of PCC and how nurse patients’ illness experiences drive quality care and patient safety.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".