Assessing trauma care systems in low-income and middle-income countries: a systematic review and evidence synthesis mapping the Three Delays framework to injury health system assessments
Bibliographic record
Abstract
BACKGROUND: The large burden of injuries falls disproportionately on low/middle-income countries (LMICs). Health system interventions improve outcomes in high-income countries. Assessing LMIC trauma systems supports their improvement. Evaluating systems using a Three Delays framework, considering barriers to seeking (Delay 1), reaching (Delay 2) and receiving care (Delay 3), has aided maternal health gains. Rapid assessments allow timely appraisal within resource and logistically constrained settings. We systematically reviewed existing literature on the assessment of LMIC trauma systems, applying the Three Delays framework and rapid assessment principles. METHODS: We conducted a systematic review and narrative synthesis of articles assessing LMIC trauma systems. We searched seven databases and grey literature for studies and reports published until October 2018. Inclusion criteria were an injury care focus and assessment of at least one defined system aspect. We mapped each study to the Three Delays framework and judged its suitability for rapid assessment. RESULTS: Of 14 677 articles identified, 111 studies and 8 documents were included. Sub-Saharan Africa was the most commonly included region (44.1%). Delay 3, either alone or in combination, was most commonly assessed (79.3%) followed by Delay 2 (46.8%) and Delay 1 (10.8%). Facility assessment was the most common method of assessment (36.0%). Only 2.7% of studies assessed all Three Delays. We judged 62.6% of study methodologies potentially suitable for rapid assessment. CONCLUSIONS: Whole health system injury research is needed as facility capacity assessments dominate. Future studies should consider novel or combined methods to study Delays 1 and 2, alongside care processes and outcomes.
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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 | 0.032 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".