Barriers and enablers for implementation of 4IR-linked diagnostics models at point-of-care in South Africa: Stakeholder engagement
Bibliographic record
Abstract
Abstract Fourth industrial revolution (4IR) technologies may improve access to disease diagnosis and treatment at point-of-care (POC). Stakeholder engagements are an important step when implementing such interventions. In this study, we report the findings of a workshop that was held with key stakeholders to determine barriers and enablers for implementing 4IR-linked POC diagnostic models in South Africa. The workshop formed part of the 2022 REASSURED Diagnostics symposium. The nominal group technique (NGT) workshop was conducted in two phases: phase 1 and phase 2 focused on determining barriers and enablers, respectively, to implementing 4IR-linked POC diagnostic models in South Africa. Stakeholders identified connectivity, offline functionality, and load shedding as some of the most important barriers, while ease of use, subsidies by the National Health Insurance, and 24-hour assistance would enable the implementation 4IR-linked POC diagnostic models. The NGT workshop provided a suitable platform for identifying important barriers and enablers to the implementation of 4IR-linked POC diagnostic models. A follow-up study should identify the best strategies for implementing 4IR-linked POC diagnostics models in underserved populations.
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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".