Determinants of Patient Satisfaction in Primary Healthcare: A Statistical Exploration
Bibliographic record
Abstract
Background: Patient satisfaction is a critical measure of healthcare quality, reflecting patients’ experiences and the extent to which their needs and expectations are met. While global studies have identified various determinants of satisfaction, research in some regions, particularly in the Gulf, remains limited. This study investigates the determinants of patient satisfaction in primary healthcare settings, focusing on socio-demographic factors and dimensions of care. Methods: This study was conducted among patients receiving services at primary healthcare centers. A systematic sampling approach yielded 398 completed questionnaires. The instrument assessed six dimensions of satisfaction: interpersonal care, technical competence, accessibility, convenience, availability, and overall satisfaction. Data analysis included descriptive statistics, t-tests, ANOVA, and exploratory factor analysis, with internal consistency measured using Cronbach’s alpha. Results: The mean satisfaction score was 35.2 (SD = 6.8). Married patients and those with college degrees reported significantly higher satisfaction. Factor analysis revealed interpersonal care, accessibility, and technical competence as the most influential dimensions. Communication quality and the time spent with healthcare providers were strongly associated with satisfaction. Approximately 64.8% of participants reported high overall satisfaction. Conclusion: Effective communication and patient-centered interactions are key determinants of satisfaction in primary care. Addressing gaps in provider-patient communication and ensuring sufficient consultation time can enhance patient experiences. These findings emphasize the need for ongoing assessment and improvement of healthcare delivery to meet diverse patient expectations
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".