Applications and challenges of simulation for healthcare operations management in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study identifies the applications and challenges of simulation for healthcare operations management in Africa using a systematic literature review and survey. Simulation has been used in Africa mainly to address problems of disease transmission and prevention and to analyze the effectiveness of diagnosis and/or treatment strategies. HIV and malaria have been studied widely using systems-dynamic, Monte Carlo, agent-based, and discrete-event simulation methods. However, the number of publications based on the first author’s affiliation revealed that out of the top 30 universities, only 5 of them are in Africa, to be specific in South Africa. This shows limited usage of simulation by researchers based in African universities. Besides, 58% of the survey participants, consisting of researchers with MSc (84%) and Ph.D. (10%) and 5–10 years of experience (70%), do not utilize their awareness of simulation for healthcare operation management due to a lack of organized data (71%), ICT infrastructure (69%), data security and privacy (68), ethical and responsible data use (65%), and attitudes of health professionals (63%). Besides, the lack of curricula on simulation in African universities, data sharing policy, awareness, and top management support have been limiting the adoption of simulation for healthcare operations management in Africa.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it