Enhancing trauma care through innovative trauma and disaster team response training: A blended learning approach in Tanzania
Bibliographic record
Abstract
BACKGROUND: In Tanzania, inadequate infrastructures and shortages of trauma-response training exacerbate trauma-related fatalities. McGill University's Centre for Global Surgery introduced the Trauma and Disaster Team Response course (TDTR) to address these challenges. This study assesses the impact of simulation-based TDTR training on care providers' knowledge/skills and healthcare processes to enhance patient outcomes. METHODS: The study used a pre-post-interventional design. TDTR, led by Tanzanian instructors at Muhimbili Orthopedic Institute from August 16-18, 2023, involved 22 participants in blended online and in-person approaches with simulated skills sessions. Validated tools assessed participants' knowledge/skills and teamwork pre/post-interventions, alongside feedback surveys. Outcome measures included evaluating 24-h emergency department patient arrival-to-care time pre-/post-TDTR interventions, analyzed using parametric and non-parametric tests based on data distributions. RESULTS: Participants' self-assessment skills significantly improved (median increase from 34 to 58, p < 0.001), along with teamwork (median increase from 44.5 to 87.5, p < 0.003). While 99% of participants expressed satisfaction with TDTR meeting their expectations, 97% were interested in teaching future sessions. The six-month post-intervention arrival-to-care time significantly decreased from 29 to 13 min, indicating a 55.17% improvement (p < 0.004). The intervention led to fewer ward admissions (35.26% from 51.67%) and more directed to operating theaters (29.83% from 16.85%), suggesting improved patient management (p < 0.018). CONCLUSION: The study confirmed surgical skills training effectiveness in Tanzanian settings, highlighting TDTR's role in improving teamwork and healthcare processes that enhanced patient outcomes. To sustain progress and empower independent trauma educators, ongoing refresher sessions and expanding TDTR across low- and middle-income countries are recommended to align with global surgery goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".