Advancing access to care: An assessment of the prehospital system in Senegal
Bibliographic record
Abstract
BACKGROUND: Most low- and middle-income countries do not have a mature prehospital system limiting access to definitive care. This study sought to describe the current state of the prehospital system in Senegal and offer recommendations aimed at improving system capacity and population access to definitive care. METHODS: Structured interviews were conducted with key informants in various regions throughout the country using qualitative and quantitative techniques. A standardized questionnaire was generated using needs assessment forms and system frameworks. Descriptive statistics were performed for quantitative data analysis, and qualitative data was consolidated and presented using ATLAS.ti. RESULTS: Two (20%) of the studied regions, Dakar and Saint-Louis, had a mature prehospital system in place, including dispatch centers and teams of trained personnel utilizing equipped ambulances. 80% of the studied regions lacked an established prehospital system. The vast majority of the population relied on the fire department for transport to a healthcare facility. The ambulances in rural regions were not part of a formal prehospital system, were not equipped with life-support supplies, and were limited to inter-facility transfers. CONCLUSIONS: While Dakar and Saint-Louis have mature prehospital systems, the rest of the country is served by the fire department. There are significant opportunities to further strengthen the prehospital system in rural Senegal by training the fire department in basic life support and first aid, maintaining cost efficiency, and building on existing national resources. This has the potential to significantly improve access to definitive care and outcomes of emergent illness in the Senegalese community.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".