Barriers and opportunities to implementation of sustainable e-Health programmes in Uganda: A literature review
Bibliographic record
Abstract
BACKGROUND: Most developing countries, including Uganda, have embraced the use of e-Health and m-Health applications as a means to improve primary healthcare delivery and public health for their populace. In Uganda, the growth in the information and communications technology industry has benefited the rural communities and also created opportunities for new innovations, and their application into healthcare has reported positive results, especially in the areas of disease control and prevention through disease surveillance. However, most are mere proof-of-concepts, only demonstrated in use within a small context and lack sustainability. This study reviews the literature to understand e-Health's current implementation status within Uganda and documents the barriers and opportunities to sustainable e-Health intervention programmes in Uganda. METHODS: A structured literature review of e-Health in Uganda was undertaken between May and December 2015 and was complemented with hand searching and a document review of grey literature in the form of policy documents and reports obtained online or from the Ministry of Health's Resource Centre. RESULTS: The searches identified a total of 293 resources of which 48 articles met the inclusion criteria of being in English and describing e-Health implementation in Uganda. These were included in the study and were examined in detail. CONCLUSION: Uganda has trialled several e-Health and m-Health solutions to address healthcare challenges. Most were donor funded, operated in silos and lacked sustainability. Various barriers have been identified. Evidence has shown that e-Health implementations in Uganda have lacked prior planning stages that the literature notes as essential, for example strategy and need readiness assessment. Future research should address these shortcomings prior to introduction of e-Health innovations.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".