Assessing trauma care health systems in low- and middle-income countries, a protocol for a systematic literature review and narrative synthesis
Bibliographic record
Abstract
BACKGROUND: Trauma represents a major global health problem projected to increase in importance over the next decade. The majority of deaths occur in low- and middle-income countries (LMICs) where survival rates are lower than their high-income country (HIC) counterparts. Health system level changes in care for injured patients have been attributed to significant improvements in care quality and outcomes in HIC settings. There is a need for further research to assess trauma care health systems in LMICs to inform health system strengthening for the care of the injured. This study aims to conduct a narrative synthesis of a systematic search of the literature on the assessment of trauma care health systems in LMICs in order to inform the further development of trauma care health system assessment. METHODS: The review will include primary quantitative, qualitative or mixed method studies and secondary literature reviews. No restriction will be placed on language or date. Reports and publications identified from the grey literature including from relevant national and international health organisations will be included. Articles will be screened by two independent reviewers with a third reviewer resolving any persisting disagreement. The search will reveal heterogenous studies not suitable for meta-analysis. A narrative synthesis of the identified papers will be conducted to identify key methodological ideas and paradigms used to assess trauma care health systems. The analysis will consider how the differing methodological approaches could be adopted to understand barriers and delays to seeking, reaching and receiving care within a "Three Delays" framework. An iterative approach will be adopted to categorise identified articles, with the results presented as both within and across study analysis. DISCUSSION: The results of the review will be disseminated through publication in a peer-reviewed academic journal. The study forms part of a PhD project. The results will inform the development of a trauma care health system assessment applicable to LMICs. As this is a review of secondary data, no formal ethical approval is required. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018112990.
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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 | 0.199 | 0.225 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".