Use of healthcare services by injured people in Khartoum State, Sudan
Bibliographic record
Abstract
BACKGROUND: Trauma care is an important factor in preventing death and reducing disability. Injured persons in low- and middle-income countries are expected to use the formal healthcare system in increasing numbers. The objective of this paper is to examine use of healthcare services after injury in Khartoum State, Sudan. METHODS: A community-based survey using a stratified two-stage cluster sampling technique in Khartoum State was performed. Information on healthcare utilisation was taken from injured people. A logistic regression analysis was used to explore factors affecting the probability of using formal healthcare services. RESULTS: During the 12 months preceding the survey a total of 441 cases of non-fatal injuries occurred, with 260 patients accessing formal healthcare. About a quarter of the injured persons were admitted to hospital. Injured people with primary education were less likely to use formal healthcare compared to those with no education. Formal health services were most used by males and in cases of road traffic injuries. The lowest socio-economic strata were least likely to use formal healthcare. CONCLUSIONS: Public health measures and social security should be strengthened by identifying other real barriers that prevent low socio-economic groups from making use of formal healthcare facilities. Integration and collaboration with traditional orthopaedic practitioners are important aspects that need further attention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".