Evaluating the Scale-Up of Antiretroviral Treatment Sites in Kwazulu-Natal Province of South Africa: Achievements and Challenges from 2005 to 2010
Bibliographic record
Abstract
In order to provide care to the increasing number of people infected with HIV, there is a need for scaling up the number of treatment sites. For the public health officials and planners, there is a need for a defined methodology to do this taking into consideration the national targets as enacted by the National Department of Health (NDOH) of South Africa. This commentary is about an evaluation conducted to review the progress made by the Province of KwaZulu-Natal in scaling-up antiretroviral treatment sites (ART). Overall, the prediction that 54 ART facilities were required for equitable distribution of antiretroviral treatment in KwaZulu-Natal had been exceeded as 89 ART sites had been established by 2010. In conclusion, the predicted number of ART sites by Wilson and Blower method was largely exceeded within 5 years of their prediction. Two major challenges are still lurking into the ART program, the accessibility by those who need it and the shortage of professional human resources particularly pharmacy staff. Innovative strategies are needed to address the shortage of health professionals and related disparities in order to increase access to ART.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".