An examination of sustainable trauma registry development in resource-constrained settings
Bibliographic record
Abstract
Injury is a major health problem worldwide, yet the distribution weighs heavily towards LMICs, where over 70% of all deaths due to injury occur.Systematizing trauma care has been used for decades to address the complex organizational problem of injury on the local, regional, and national scale.Trauma registries, which are databases that document information related to the injury event, process of care, and outcome of injured patients, are a critical first step in the development of these trauma systems and vital to informed decision-making across the entire continuum of trauma care.However, the implementation and operation of trauma registries requires a substantial investment of time, money, and effort that many hospitals in LMICs or other resource-constrained areas do not have.Those that were able to develop trauma registries despite resource limitations have often found the process challenging, riddled not only with barriers during the process of establishment, but also during registry operation and data analysis and dissemination.The research presented in this dissertation aims to do address three overarching objectives: 1) to better understand where and how trauma registries are being operated in LMICs, 2) to identify and propose solutions to the challenges that inhibit their full functionality, and 3) to identify the different barriers and facilitators to trauma registry implementation and operation and investigate how these affect long-term sustainability.Four papers contribute to the makeup of this dissertation.In the first paper, a scoping review of trauma registry implementation and operation in LMICs is carried out to determine the geographical distribution of trauma registries and improve our understanding of the variation in operation across contexts.In the second paper, an overview is presented of the challenges that stand in the way to maximizing the full potential of trauma registries in LMICs.The third and fourth papers examine barriers and facilitators to trauma registry implementation and operation in resource-constrained settings through a questionnaire and case study research.Overall, this dissertation contributes to an improved understanding of trauma registries in both LMICs and similarly resource-constrained settings, and even more critically the factors that impede or encourage trauma registry sustainability.
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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 | 0.100 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".