South–south collaboration in scale-up of HIV care: building human capacity for care
Bibliographic record
Abstract
OBJECTIVES: South-south collaborations in building human resource capacity have been inadequately emphasized globally despite the growing experience among resource-poor countries in scaling up HIV care and the funding to implement programmes. This paper aims to describe one such successful collaboration, in which a model of HIV care was developed in Haiti, adapted and expanded to Lesotho, and allowed the effective scale-up of HIV and other treatment services in a rural African setting. METHODS: Institutional experiences and lessons learned over a 10-year period in Haiti and a 3-year period in Lesotho are discussed. RESULTS: The Haiti-Lesotho collaborative model shows that human resource capacity can be built using creative partnerships and exchanges between developing countries, particularly with financial support from the north. The collaboration allows for the sharing of experiences and solutions through perspectives and experiences that are unique to developing countries. Healthcare workers in Haiti and Lesotho have established meaningful and fruitful cross-country working relationships, job satisfaction and retention has been improved and a sense of solidarity developed. The model of care developed in Haiti was successfully adapted, replicated and implemented in Lesotho. CONCLUSION: South-south collaborations are an important way for countries with established experience managing HIV in resource-poor settings to share their skills in a collaborative fashion with other nations facing similar disease problems and infrastructural challenges. This model for scaling up effective practice should be encouraged and supported by programme funders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.040 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".