Delivery of HIV care during the 2007 post-election crisis in Kenya: a case study analyzing the response of the Academic Model Providing Access to Healthcare (AMPATH) program
Bibliographic record
Abstract
BACKGROUND: Widespread violence followed the 2007 presidential elections in Kenya resulting in the deaths of a reported 1,133 people and the displacement of approximately 660,000 others. At the time of the crisis the United States Agency for International Development-Academic Model Providing Access to Healthcare (USAID-AMPATH) Partnership was operating 17 primary HIV clinics in western Kenya and treating 59,437 HIV positive patients (23,437 on antiretroviral therapy (ART)). METHODS: This case study examines AMPATH's provision of care and maintenance of patients on ART throughout the period of disruption. This was accomplished by implementing immediate interventions including rapid information dissemination through the media, emergency hotlines and community liaisons; organization of a Crisis Response leadership team; the prompt assembly of multidisciplinary teams to address patient care, including psychological support staff (in clinics and in camps for internally displaced persons (IDP)); and the use of the AMPATH Medical Records System to identify patients on ART who had missed clinic appointments. RESULTS: These interventions resulted in the opening of all AMPATH clinics within five days of their scheduled post-holiday opening dates, 23,949 patient visits in January 2008 (23,259 previously scheduled), uninterrupted availability of antiretrovirals at all clinics, treatment of 1,420 HIV patients in IDP camps, distribution of basic provisions, mobilization of outreach services to locate missing AMPATH patients and delivery of psychosocial support to 300 staff members and 632 patients in IDP camps. CONCLUSION: Key lessons learned in maintaining the delivery of HIV care in a crisis situation include the importance of advance planning to develop programs that can function during a crisis, an emphasis on a rapid programmatic response, the ability of clinics to function autonomously, patient knowledge of their disease, the use of community and patient networks, addressing staff needs and developing effective patient tracking systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".