Determinants of patient satisfaction among outpatients with chronic illnesses in the region of medina, Saudi Arabia: a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Patient satisfaction is important for chronic care quality; however limited evidence exists regarding satisfaction patterns and demographic predictors among outpatients with chronic illnesses in Saudi Arabia. This study investigated patient satisfaction levels and identified demographic factors affecting satisfaction in chronic care settings. METHODS: A cross-sectional survey was conducted among 397 outpatients with chronic illnesses attending healthcare facilities in Medina, Saudi Arabia (August-September 2024). The validated Patient Satisfaction Questionnaire Short Form (PSQ-18) was administered electronically in Arabic and English. Demographic predictors were analyzed using ordinal logistic regression with satisfaction tertiles. Doctor-patient interaction correlations were assessed using Spearman coefficients. RESULTS: Participants included 213 females (53.6%) and 184 males (46.4%), with median age 45 years. Overall PSQ-18 satisfaction score was 64.4% of maximum possible. Age was the primary satisfaction predictor, with patients aged 45-60 years showing 2.31 times higher odds of superior satisfaction compared to younger patients (18-30 years, P-value = 0.010). Doctor-patient interaction measures showed strong correlations with overall satisfaction: physical comfort (ρ = 0.635), respect and empathy (ρ = 0.602), and comfort asking questions (ρ = 0.500, all P-value < 0.001). Gender and employment status showed no significant associations with satisfaction levels. Accessibility and convenience scored lowest (60% satisfaction), while interpersonal manner, communication, and financial aspects achieved highest scores (70% satisfaction). CONCLUSIONS: Age-appropriate care delivery and enhanced doctor-patient interactions represent the most promising targets for improving chronic care satisfaction. Healthcare systems should prioritize interpersonal care training and accessibility improvements to optimize patient experiences across all age groups.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".