The Relationship Between Staff Nurses' Perceptions of Nurse Manager Caring Behaviors and Patient Experience
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore the relationship between staff nurses' perceptions of nurse manager caring behaviors and patient experience. BACKGROUND: Despite numerous interventions aimed at changing the provision of patient care to improve care quality, patient experience scores have remained moderate. Little research has been conducted exploring how caring relationships in the professional practice environment might play a role in the patient experience of care. METHODS: A cross-sectional, correlational design was used to examine the relationship between staff nurses' perceptions of nurse manager caring behaviors as measured by the Caring Assessment Tool-Administration (CAT-Adm) and acute-care patient experience using the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores. RESULTS: There was a positive relationship between the staff nurses' perceptions of nurse manager caring behaviors and patients' HCAHPS overall hospital rating. There also was a positive relationship between the CAT-Adm scores and nurse manager visibility. CONCLUSION: Departments had higher HCAHPS overall hospital rating when the staff nurses perceived their unit manager as caring. In addition, the more staff nurses actually visualized their nurse manager during a shift, the more they perceived their nurse manager as caring.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".