Using livelihoods to support primary health care for South Sudanese refugees in Kiryandongo, Uganda
Bibliographic record
Abstract
Introduction: Conflict in South Sudan has displaced 2.3 million people, of whom 789,098 (35%) have taken refuge in Uganda – a country that allows refugees to work, own property, start their own businesses and access public health services. In this context, refugees have identified livelihoods and primary health care as key priorities for their wellbeing. Objective: Building on previous research in South Sudan and Uganda, the objective of our current work is exploring how income-generating livelihood activities and other interventions can be used to support primary health care for South Sudanese refugees in Kiryandongo District, Uganda. Methods: We drew on existing secondary data and five scoping visits to the refugee settlements in Kiryandongo and northern Uganda to formulate our approach. Results: In Kiryandongo District, primary health care and livelihoods can best be supported by an integrated combination of 1) providing standardised training to local Village Health Teams (VHTs); 2) helping organise VHTs into village savings and loan association groups; and 3) supporting VHTs with training to establish sustainable income-generating activities. Conclusions: Integrated interventions that address income-generating activities for community health workers can meet the basic needs of front-line volunteer primary health care staff and better enable them to improve the health of their communities. Keywords: primary health care, refugees, livelihoods, South Sudan, Uganda, Kiryandongo
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".