Mobile Technology and Its Impact on Healthcare Access Among Rural Senegalese Herders: A Systematic Review of Literature
Bibliographic record
Abstract
Mobile technology has emerged as a critical tool in enhancing healthcare access for rural populations globally. In Senegal, particularly among herders who are often isolated and geographically dispersed, mobile applications can play a significant role in improving health outcomes. A comprehensive search strategy was employed in electronic databases including PubMed, Scopus, and Web of Science. Studies were included if they utilised mobile technology to improve healthcare access in rural areas, specifically among Senegalese herders. Eligible studies were assessed for methodological quality using the Newcastle-Ottawa Scale. Mobile applications have shown a positive impact on healthcare access with an average increase of 30% in patient consultations and a significant reduction (p < 0.05) in travel time to nearest health facility, particularly among herders living in remote areas. The integration of mobile technology into healthcare systems has the potential to significantly improve accessibility for rural Senegalese herders by reducing barriers such as geographical isolation and financial constraints. Healthcare providers should consider implementing tailored mobile health solutions that are culturally relevant and user-friendly, while policymakers can support these initiatives through policy frameworks that promote digital inclusion in healthcare delivery. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
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.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".