Health Status and Quality of Health Care Services of Congolese Refugees in Nakivale, Uganda
Bibliographic record
Abstract
Physical and emotional wellness, as well as access to healthcare, are foundations for successful resettlement. Without feeling healthy, it is difficult to work, to go school, or take care of a family. Many factors can affect refugee health, including geographic origin and refugee camp conditions. Refugees may face a wide variety of acute or chronic health issues (Office of Refugee Resettlement, ORR Annual Report to Congress 2014; http://www.acf.hhs.gov). Resettlement of refugees in Uganda is usually supported by concerted efforts of UNHCR, Governments through the Office of the Prime Minister, OPM with support from host communities, local and international Non-Governmental Organizations. Due to resource constraints and local factors, immigrants are often subjected to poor living conditions which coupled with inadequacy inessential medical supplies might significantly affects quality of care and health service delivery and hence, rendering refugees to poor health status. This study was conducted from 2013-2014 to assess the determinants of health status of Congolese refugees living in Nakivale refugee settlement, in Isingiro district- South Western Uganda. A cross-sectional study design was used involving mixed techniques of both qualitative and quantitative KAP survey. The study focussed on Congolese refugee population in Nakivale Refugee settlement. 2401 key informants’ interviews and 8 focus group discussions respectively were conducted targeting service providers and beneficiaries/Congolese refugees in this case. The data was analysed using SPSS ver.20, 2011. Although majority (97%) of respondents sought medical services from established health facilities, findings confirm a high level of ill health prevalence among Congolese refugees in Nakivale camp, however, the difference in health services and perceived health status in camp versus the one in DRcongo is insignificant ( p=0.000) with respondents perceiving their health status as worse than when they were their own Country before the resettlement. Identified key challenges affecting access & uptake of available health services includes: language barrier; inadequate drugs; and the long distances to access health facilities. The health status of refugees could be improved by addressing the challenges related to language, drug supplies in addition to humanising conditions of shelter, providing appropriate waste disposal facilities while proving adequate food rations and clean & safe drinking water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".