Point of Care Ultrasound (POCUS) Utilization and Barriers by Senior Emergency Medicine and Critical Care Residents at Two Teaching Referral Hospitals, Addis Ababa, Ethiopia
Bibliographic record
Abstract
Background. POCUS has become an integral part of the practice of emergency medicine. POCUS is a highly focused, limited, goal-directed exam with the expressed purpose of answering selected questions used at the bedside for critically ill patients who are not stable. We aimed to assess POCUS utilization and barriers by senior-year emergency medicine and critical care residents at two tertiary academic and referral hospitals in Addis Ababa, Ethiopia. Methodology. A cross-sectional study was conducted from June 1 to August 30, 2022 in St Paul’s Hospital Millennium Medical College and Tikur Anbessa Specialized Hospital using an electronic survey of senior-year (second and third years) emergency medicine and critical care residents. Data were collected using Goggle form, exported to SPSS version 24, and then analyzed. Result. Seventy-six residents out of 78 (97.4%) responded to the online survey. The mean age was 29.9 years with an SD of 2.87. Fifty-six residents (73.7%) were male and 45 (59.2%) were year 2 residents. Sixty-one (76.3%) had previous POCUS training. Fifty residents (82.0%) received training from the classroom. Twenty-seven residents (35.5%) rated their current level of knowledge as good for sterile transducer techniques, 28 (36.8%) rated fair for their knobology, and 27 (35.5%) rated very good for their transducer selection knowledge. Thirty-two (42.1%) rated very good about their ability to interpret IVC. 26 (34.2%) responded that they had good ability to interpret FAST/EFAST. Forty-nine (64.5%) residents claimed lack of an ultrasound machine followed by 33 (43.4%) lack of organized curriculum were the main barriers to POCUS utilization. Forty-two (55.3%) residents preferred to complete face-to-face teaching, while 33 (43.4%) preferred blended learning both face-to-face and online. Conclusion. POCUS is performed by the majority of EMCC residents. The most frequent scans performed by residents were FAST, IVC, and lung scans. Lack of ultrasound machine and organized curriculum was the main barrier to US utilization. Availability of equipment, face-to-face training, and having an organized curriculum are recommended by residents to improve their skills in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".