Tuberculosis testing for healthcare workers in South Africa: A health service analysis using Porter's Five Forces Framework
Bibliographic record
Abstract
South Africa has one of the world's highest tuberculosis (TB) incidence rates. Healthcare workers (HCWs) are at particularly high risk of developing active TB compared to the general population, due to their occupational exposures. International guidelines support the routine screening of HCWs for active TB as an effective strategy to reduce the TB burden in high prevalence countries. However, it is estimated that only two-thirds of HCWs in South Africa will utilize available TB services at any point in their career. In this study, multiple data sources including semi-structured interviews, focus groups, observation, site visits, and secondary data analysis were synthesized and analyzed using Porter's Five Forces Framework of Competitive Position Analysis. The outcomes of this systematic evaluation found TB services available to HCWs in South Africa to be an industry with high rivalry, moderate -to -high bargaining power of the suppliers and a threat of substitutes, low bargaining power of buyers and a limited threat of new entrants. Value opportunities presented through these five forces can enhance the utilization of TB services, reducing TB-associated morbidity and mortality among this high risk, yet often neglected and under-researched population.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".