Qualitative assessment of South African healthcare worker perspectives on an instrument-free rapid CD4 test
Bibliographic record
Abstract
BACKGROUND: Accurate measurement of CD4 cell counts remains an important tenet of clinical care for people living with HIV. We assessed an instrument-free point-of-care CD4 test (VISITECT® CD4) based on a lateral flow principle, which gives visual results after 40 min. The test involves five steps and categorises CD4 counts as above or below 350 cells/μL. As one component of a performance evaluation of the test, this qualitative study explored the views of healthcare workers in a large women and children's hospital on the acceptability and feasibility of the test. METHODS: Perspectives on the VISITECT® CD4 test were elicited through in-depth interviews with eight healthcare workers involved in the performance evaluation at an antenatal care facility in Johannesburg, South Africa. Audio recordings were transcribed in full and analysed thematically. RESULTS: Healthcare providers recognised the on-going relevance of CD4 testing. All eight perceived the VISITECT® CD4 test to be predominantly user-friendly, although some felt that the need for precision and optimal concentration in performing test procedures made it more challenging to use. The greatest strength of the test was perceived to be its quick turn-around of results. There were mixed views on the semi-quantitative nature of the test results and how best to integrate this test into existing health services. Participants believed that patients in this setting would likely accept the test, given their general familiarity with other point-of-care tests. CONCLUSIONS: Overall, the VISITECT® CD4 test was acceptable to healthcare workers and those interviewed were supportive of scale-up and implementation in other antenatal care settings. Both health workers and patients will need to be oriented to the semi-quantitative nature of the test and how to interpret the results of tests.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".