A survey of barriers and facilitators to ultrasound use in low- and middle-income countries
Bibliographic record
Abstract
Point-of-care ultrasound has the potential to help inform assessment, diagnosis, and management of illness in low- and middle-income countries (LMIC). To better understand current ultrasound use, barriers and facilitators to use, and perceptions and practices in LMIC, we conducted an anonymous online global survey targeting healthcare providers training and using ultrasound in LMIC. A total of 241 respondents representing 62 countries participated and most were physicians working in publicly-funded urban tertiary hospitals in LMIC. Most had received ultrasound training (78%), reported expertise (65%) and confidence (90%) in ultrasound use, and had access to ultrasound (88%), utilizing ultrasound most commonly for procedures and for evaluations of lungs, heart, and trauma. Access to an ultrasound machine was reported as both the top barrier (17%) and top facilitator (53%); other common barriers included access to education and training, cost, and competition for use and other common facilitators included access to a probe, gel, and electricity, and acceptance by healthcare providers, administrators, and patients. Most (80%) noted ultrasound access was important and 96% agreed that ultrasound improves quality of care and patient outcomes. Improving access to low-cost ultrasound equipment is critical to increasing ultrasound use among those who are trained.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".