From face-to-face to e-learning: transitioning to new training models to strengthen the health system by supporting primary healthcare workers in low- and middle-income countries
Bibliographic record
Abstract
Reliance on purely face-to-face in-service training for primary healthcare workers in low- and middle-income countries is increasingly unsustainable. The COVID-19 pandemic accelerated the transition of the University of Cape Town Knowledge Translation Unit's Practical Approach to Care Kit programme from a facility-based cascade model to online and blended learning formats.This paper analyses the implementation of this transition across 29 courses between 2020 and 2023 in South Africa. Using the Health System Process Goals framework, we reflect on the challenges and enablers of e-learning, shifting the focus from digital training as a standalone technical solution to a systemic enabler of health system strengthening.While e-learning expanded access and standardised content, successful implementation relied on addressing systemic barriers. Key learnings include the necessity of subsidised ('reverse-billed') data to ensure equitable access; the superiority of a 'blended' pedagogical model that combines digital content with peer interaction and in-person technical support and the value of automated reporting for workforce management. The systemic barriers included the lack of protected time for learners, which risks placing an inequitable burden on the workforce and reliance on donor funding, challenging long-term institutionalisation.For e-learning to effectively strengthen the health system, it must be integrated into administrative workflows and budget lines. We provide actionable recommendations for Ministries of Health, funders and implementers, advocating for a transition to government-owned platforms, accredited blended learning models and policy that mandates protected time for capacity development.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".