Predictors of patient satisfaction in an emergency care centre in central Saudi Arabia: a prospective study
Bibliographic record
Abstract
AIM: This study aimed to (i) assess the level of patient satisfaction and its association with different sociodemographic and healthcare characteristics in an emergency care centre (ECC) in Saudi Arabia and (ii) to identify the predictors of patients' satisfaction. METHODS: A prospective cohort study of 390 adult patients with Canadian triage category III and IV who visited ECC at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between 1 July and end of September 2011 was conducted. All patients were followed up from the time of arrival at the front desk of ECC until being seen by a doctor, and were then interviewed. Patient satisfaction was measured using a previously validated interview-questionnaire, within two domains: clarity of medical information and relationship with staff. Patient perception of health status after as compared with before the visit, and overall life satisfaction were also measured. Data on patient characteristics and healthcare characteristics were collected. Multiple linear regression analysis was used, and significance was considered at p≤0.05. RESULTS: One-third (32.8%) of patients showed high level of overall satisfaction and 26.7% were unsatisfied, with percentage mean score of 70.36% (17.40), reflecting moderate satisfaction. After adjusting for all potential confounders, lower satisfaction with the ED visit was significantly associated with male gender (p<0.001), long waiting time (p=0.032) and low perceived health status compared with status at admission (p<0.001). Overall life satisfaction was not a significant predictor of patient satisfaction. CONCLUSIONS: An appreciation of waiting time as the only significant modifiable risk factor of patient satisfaction is essential to improve the healthcare services, especially at emergency settings.
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 | 0.001 | 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".