Socioeconomic Factors Affecting the Level of Satisfaction with the Health Services Received in Primary Health Care Centers in Amman, Jordan
Bibliographic record
Abstract
Patient satisfaction is a crucial component and major indicator of health care services quality. Therefore, evaluating patient satisfaction with visits to primary health care centers (PHCCs) is crucial for determining the quality of health care services provided. This study aimed to measure the level of satisfaction of patients with medical care and services received in Amman's primary health care facilities (PHCCs). The questionnaire including the subscales of “satisfaction with the services and features of PHCCs,” “satisfaction with the services of the medical staff and employees at PHCCs,” and “satisfaction with the cost of services provided at PHCCs,” was used in this study on 385 patients who visited the primary health care centers in Amman. All analyses in the study were carried out using Microsoft Office Excel, and the Statistical Package for the Social Sciences (SPSS). The variables affecting Amman patients' satisfaction were analyzed using a binary regression analysis model. The results were shown as adjusted odds ratios (AOR) and were deemed significant when the P-value was less than 0.05. The results of the study indicated that (77.6%) of patients were satisfied with the PHCCs services. The subscale of satisfaction with the services of the medical staff and employees at PHCCs received a rating of (80.4%) compared to (78.8%) for satisfaction with the cost of services provided at the PHCCs, and (75.4%) for satisfaction with the services and features of the PHCCs. Male participants were more satisfied with PHCCs services compared to females (P = 0.012 and 95% CI= .26, .84). Employed participants were also more satisfied with PHCCs services (P = 0.031 and 95% CI = 1.06, 3.71). The findings of this study will be valuable for policymakers and strategic planners, providing them with direct insights into the key areas that should be prioritized to provide the best possible health care for the beneficiaries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".