“It’s a Mixed Bag”: An Interpretive Description of the Person-Centred Mental Health Nursing Care Received by Individuals During an Inpatient Hospitalization
Bibliographic record
Abstract
The aim of this study was to explore individuals' perspectives on the person-centred nursing care they received during a recent mental health inpatient hospitalization. Eight individuals who were admitted to an inpatient unit in the previous 12 months participated in the study. The study was guided by the Person-centred Practice Framework and used the methodology of Interpretive Description. The constant comparative method supported the analysis resulting in three themes: 1) The rare, but precious, moments of person-centred care, 2) The relationship with my nurse: A fluctuating connection, and 3) The pearls and perils of the care environment. Those interviewed described few person-centred experiences. The fragile relationships between participants and their nurses and the fear experienced in the care environment may have contributed to this finding. Our findings are consistent with existing evidence, as the challenges of implementing person-centred care are broad in scope and not easily managed. Study results may encourage nurses to critically reflect on their own practice and consider meaningful changes in how they work. Further, health organizations may consider how they can better support nurses in the delivery of person-centred care through policy development, staff training, and creating environments that foster shared decision-making, safety, and meaningful engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".