Preventing Disruptions in HIV Service Delivery to Key Populations During Project Transition From an International to a Local Implementing Partner: A Case Study From Zambia
Bibliographic record
Abstract
In the management of chronic conditions like HIV, the continuity of service delivery is necessary to achieve desired outcomes, such as HIV viral load suppression, behavioral change, improved health, and client satisfaction. The transition phase-when a project closes and another starts-is a potential period of service delivery disruption. Active management of this transition period is important to prevent disruptions, especially for key populations who may be stigmatized and have limited options for accessing HIV services. We analyzed this transition period between July and December 2022 between 2 projects that provided HIV prevention services, management of sexually transmitted infections, and linkage to HIV treatment and other complementary services to key populations in Zambia. To ensure a smooth project transition, we implemented a set of interventions, including joint planning for project transition, strategic leadership, trust-building initiatives, active community and stakeholder engagement, repeated stakeholder reassurance, open communication, and transparent data sharing. After transitioning to the new project, we noted that all 3 service types of interest experienced at least a 20% increase over the levels achieved in the last month of the closing project. This increase contrasts with the assumption that all service types delivered through project structures would decline to zero persons reached within 2 months of project closing if the next project did not commence seamlessly. The decrease in service delivery was averted with the intentional transition interventions. Additionally, we recorded operational gains, such as stakeholder satisfaction, adequate assets transfer, stability in project service delivery location, and reduced personnel anxiety. We conclude that active multipartite management of the transition phase for projects is essential for ensuring uninterrupted service delivery and sustaining good outcomes for clients. Donors, health system managers, and program managers should actively require and design sound transition management plans as part of their program designs. In the aftermath of recent abrupt cuts in US Government development sector funding that allowed no planned transitions, it is important that surviving programs carefully imbibe lessons shared in this paper to protect years-and sometimes decades-of program gains.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".