Emergency and trauma care in Pakistan: a cross-sectional study of healthcare levels
Bibliographic record
Abstract
BACKGROUND: The importance of emergency medical care for the successful functioning of health systems has been increasingly recognised. This study aimed to evaluate emergency and trauma care facilities in four districts of the province of Sindh, Pakistan. METHOD: We conducted a cross-sectional health facility survey in four districts of the province of Sindh in Pakistan using a modified version of WHO's Guidelines for essential trauma care. 93 public health facilities (81 primary care facilities, nine secondary care hospitals, three tertiary hospitals) and 12 large private hospitals were surveyed. Interviews of healthcare providers and visual inspections of essential equipment and supplies as per guidelines were performed. A total of 141 physicians providing various levels of care were tested for their knowledge of basic emergency care using a validated instrument. RESULTS: Only 4 (44%) public secondary, 3 (25%) private secondary hospitals and all three tertiary care hospitals had designated emergency rooms. The majority of primary care health facilities had less than 60% of all essential equipments overall. Most of the secondary level public hospitals (78%) had less than 60% of essential equipments, and none had 80% or more. A fourth of private secondary care facilities and all tertiary care hospitals (n=3; 100%) had 80% or more essential equipments. The average percentage score on the physician knowledge test was 30%. None of the physicians scored above 60% correct responses. CONCLUSIONS: The study findings demonstrated a gap in both essential equipment and provider knowledge necessary for effective emergency and trauma care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".