Factors associated with teletrauma utilization in rural areas: a review of the literature
Bibliographic record
Abstract
INTRODUCTION: Trauma patients residing in rural areas face increased challenges to accessing timely and appropriate health services as a result of large geographic distances and limited resource availability. Virtual trauma supports, coined 'teletrauma', are one solution offered to address gaps in rural trauma care. Teletrauma represents a new and innovative solution to addressing health system gaps and optimizing patient care within rural settings. Here, the authors synthesize the empirical evidence on teletrauma research. METHODS: A review of literature, with no date limiters, was guided by Arksey and O'Malley's (2005) scoping review methodology. The aim of the review was to provide an overview of the current landscape of teletrauma research while identifying factors associated with utilization. RESULTS: Following a systematic search of key health databases, 1484 articles were initially identified, of which 28 met the inclusion criteria and were included for final analysis. From the review of the literature, the benefits of teletrauma for rural and remote areas were well-recognized. Several factors were found to be significantly associated with teletrauma utilization, including younger patient age, penetrating injury, and higher injury or illness severity. Lack of access to resources and clinician characteristics were also identified as reasons that sites adopted teletrauma services. CONCLUSION: By identifying factors associated with teletrauma utilization, teletrauma programs may be used more judiciously and effectively in rural areas as a means of enhancing access to definitive trauma care in rural areas. Gaps in current knowledge were also identified, along with recommendations for future research.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".