The Person‐Centred Care Practices of Mental Health Nurses: A Cross‐Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Person-centred care can result in improved patient satisfaction and health outcomes; however, operationalisation has been challenging in mental health settings. AIM: To describe person-centred mental health nursing practices and associated factors. METHODS: The study was underpinned by The Person-Centred Practice Framework. All mental health and addictions registered nurses in one Canadian province were invited to complete the Person-centred Practice Inventory-Staff (PCPI-S) and a short demographic questionnaire. EQUATOR network recommendations for quantitative (STROBE) data were followed. RESULTS: = 0.451, p < 0.001) association between the practice environment domain score and the person-centred process domain score, which was the measure of delivery of person-centred care. A regression model explained 76.1% of the variance in delivery of person-centred care. Significant predictors were: (1) the prerequisites domain score of the PCPI-S, (2) the environment domain score of the PCPI-S and (3) nurses' relationship with their manager. DISCUSSION: Findings from our study provide strong support for the use of the Person-centred Practice Framework in mental health nursing, particularly in the context of Canadian mental health and addictions services. Study results align with existing evidence that also reported generally favourable PCPI-S scores with comparatively lower scores in the practice environment domain. RECOMMENDATIONS: To address practice environment concerns, future research should focus on the context within which care takes place and its impact on the delivery of person-centred 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".