Indicators of Healthcare Services Utilization among the Syrian Refugee Population in Jordan: An Observational Study
Bibliographic record
Abstract
BACKGROUND: Sufficient healthcare services utilization among the Syrian refugee population is one of the most important human rights. Vulnerable populations, such as refugees, are often deprived of sufficient access to healthcare services. Even when healthcare services are accessible, refugees vary in their level of utilization of these services and their health-seeking behavior. PURPOSE: This study aims to examine the status and indicators of healthcare service access and utilization among adult Syrian refugees with non-communicable diseases residing in two refugee camps. METHODS: The cross-sectional descriptive design was conducted by enrolling 455 adult Syrian refugees residing in the Al-Za'atari and Azraq camps in northern Jordan, using demographical data, perceived health, and the "Access to healthcare services" module, which is a part of the Canadian Community Health Survey (CCHS). A logistic regression model with binary outcomes was used to explore the accuracy of the variables influencing the utilization of healthcare services. The individual indicators were examined further out of 14 variables, according to the Anderson model. Specifically, the model consisted of healthcare indicators and demographic variables to find out if they have any effect on healthcare services utilization. RESULTS: Descriptive data showed that the mean age of the study participants (n = 455) was 49.45 years (SD = 10.48), and 60.2% (n = 274) were females. In addition, 63.7% (n = 290), of them were married; 50.5% (n = 230) held elementary school-level degrees; and the majority 83.3% (n = 379) were unemployed. As expected, the vast majority have no health insurance. The mean overall food security score was 13 out of 24 (±3.5). Difficulty in accessing healthcare services among Syrian refugees in Jordan's camps was significantly predicted by gender. "Transportation problems, other than fee problems" (mean 4.25, SD = 1.11) and "Unable to afford transportation fees" (mean 4.27, SD = 1.12) were identified as the most important barriers to accessing healthcare services. CONCLUSION: Healthcare services must imply all possible measures to make them more affordable to refugees, particularly older, unemployed refugees with large families. High-quality fresh food and clean drinking water are needed to improve health outcomes in camps.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".