Factors Affecting Patient Satisfaction With Emergency Department Care: An Italian Rural Hospital
Bibliographic record
Abstract
BACKGROUND: In the emergency department satisfaction is strictly linked to the role of the nurses, namely the first interface between patients and hospital services. OBJECTIVES: The purpose of the study was to identify areas of emergency nursing activity associated with minor or major patient satisfaction. METHODS: A descriptive cross-sectional study was conducted from December 2010 - May 2011, in the rural hospital of Orbetello, Tuscany (Italy). Convenience sampling was used to select patients, namely patients presenting at the emergency unit in the study period. The Consumer Emergency Care Satisfaction Scale was used to collect information on two structured subscale (Caring and Teaching). RESULTS: 259 questionnaire were collected. Analysis indicated that only two characteristics significantly influenced overall satisfaction: "receiving continuous information from personnel about delay" positively effect (OR=7.98; p=0.022) while "waiting time for examination" had a negative effect (OR 0.42; p=0.026). CONCLUSIONS: The study was the first conduced in Italy using this instrument that enabled to obtain much important information about patient satisfaction with nursing care received in the emergency department. The results showing improvements must be related to educational aspects, such as explaining patients the colour waiting list, and communication towards patients, such as informing about emergences that cause queue.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".