How does Jordanian patients’ satisfaction with emergency nursing care associated with their knowledge of the triage system and expected time to wait?
Bibliographic record
Abstract
Objective: Emergency departments (EDs) are critical to healthcare systems, yet in Jordan, overcrowding and resource limitations challenge care quality. This study assessed how Jordanian patient satisfaction with nursing care at EDs related to their understanding of triage systems and wait times. Method: A prospective cross-sectional design was used. Data were collected from largest two healthcare hospitals in Jordan which utilizing Canadian triage system. A convenience sampling method was utilized. All adult patients (≥18 years) were included. However, patient’s triaged at level 1 (resuscitation) or 2 (emergent) based on Canadian triage system, pediatric patients, and/or those with documented history of psychiatric illness were excluded. Valid and reliable tools were used. Result: The mean age of patients was 37.6 years (SD=11.4), with a mean satisfaction score of 15.79/20 (SD=3.22), reflecting high satisfaction. Most patients (61.3%) were unaware of triage processes; however, their satisfaction with nursing care was related with triage understanding (P<0.05). Younger patients (t=2.045, P<0.05), Jordanian nationals (t=1.817, P<0.05), unmarried individuals (F=3.32, P<0.05), and government-sector workers (F=3.42, P< 0.05) reported significantly higher satisfaction than others. Conclusion: Enhancing patient satisfaction in EDs relies on optimizing nursing care, particularly through staff training in triage systems and patient education about triage processes. Implementing standardized protocols, along with accessible educational materials for patients while they are in the waiting room, is critical to addressing care gaps and ensuring sustainable improvements.
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 | 0.004 |
| 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 | 0.001 |
| 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".