Mixed methods process evaluation of pilot implementation of the African Federation for Emergency Medicine trauma data project protocol in Ethiopia
Bibliographic record
Abstract
INTRODUCTION: The African Federation for Emergency Medicine Trauma Data Project (AFEM-TDP) has created a protocol for trauma data collection in resource-limited settings using a clinical chart with embedded standardized data points that facilitates a systematic approach to injured patients. We performed a process evaluation of the protocol's implementation at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia to provide insights for adapting the protocol to our setting. METHODS: During the pilot implementation period, the quality of collected data was assessed. Structured key informant interviews about participant experiences and perceptions of the protocol implementation were then conducted. Interviews were analysed using a SWOT model. RESULTS: During pilot data collection, the overall capture rate was 21%. Variables collected with high frequency included demographics, vital signs and ED diagnosis, while mechanism of injury and ED disposition were often missed. Key informant interviews identified Strengths, Weaknesses, Opportunities and Threats to the protocol. Strengths included improved patient care, enhanced training for junior providers and facilitated data collection. Weaknesses included inadequate supervision and challenges relating to the physical size of the form, which resulted in missing data. Opportunities included retrospective research and quality improvement work. Threats included perceived lack of a local champion, poor buy-in from other hospital departments and need for ongoing financial support. CONCLUSION: A mixed methods process evaluation is an invaluable tool when implementing novel data collection protocols, especially in resource-limited settings. We determined early successes and challenges of the implementation of the AFEM-TDP protocol and generated strategies to adapt the protocol to better suit our setting. Lessons from this process evaluation may be informative for other researchers designing and implementing similar data collection protocols.
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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 | 0.394 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".