Patterns of computer and Internet use and its association with HIV knowledge in selected countries in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Healthcare systems in Sub-Saharan Africa (SSA) are fraught with numerous governance and infrastructural issues including lack of access to quality care and health worker shortage. Policy makers are becoming increasingly interested in adopting novel technologies such as web-based interventions within the scope of e-Health to bridge the gaps in care delivery in a cost-effective and sustainable manner. Successful implementation of these policies is reliant on evidences regarding people's access to these technologies, which are scarce for countries in SSA. OBJECTIVES: To 1) investigate the variation in the prevalence of accessing computer and internet across regional and socioeconomic groups, and 2) assess association between ever accessing computer and internet and knowledge of routes and risk factors of HIV transmission in selected SSA countries. METHODS: We analyzed cross-sectional datasets from UNICEF Multiple Indicator Cluster Surveys. Participants were16,194 men and 39,121 women from Ghana, Guinea Bissau, Malawi and Zimbabwe. Main outcome variable was ever-accessing computer and Internet for any purpose. Associations were assessed by multivariable regression methods. RESULTS: Lifetime computer usage in Ghana, Guinea Bissau, Malawi and Zimbabwe was respectively 21.5%, 13.4%, 12.3%, 28.4% among men, and 12.5%, 8.3%, 4.8%, 20.5% among women, and that of internet was 14.9%, 11.7%, 10.8%, 34% among men, and 6.4%, 6.9%, 4.2%, 21.6% among women in the aforementioned order. Participants who reported ever using computer and Internet were more likely to have higher knowledge regarding the transmission of HIV compared to those who did not. CONCLUSIONS: Prevalence of lifetime access to computer and Internet was considerably low in all four countries. Several socioeconomic factors appeared to be associated with the access to computer and Internet, addressing which might prove beneficial for the successful expansion e-Health in these countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".