Characterizing the limited use of point-of-care ultrasound in Colombian emergency medicine residencies
Bibliographic record
Abstract
Emergency medicine (EM) is a growing specialty in Colombia with five residency programs in the country. EM leadership is interested in incorporating point-of-care (POC) ultrasound into a standardized national EM residency curriculum. This study is a nationwide survey of Colombian EM residents designed to explore the current state of POC ultrasound use within EM residencies and examine specific barriers preventing its expansion.We conducted a mix-methodology study of all available current EM residents in the five EM residencies in Colombia. The quantitative survey assessed previous ultrasound experience, current use of various applications, desire for further training, and perceived barriers to expanded use. Focus group discussions (FGDs) were conducted with current EM residents to gather additional qualitative insight into their practice patterns and perceived barriers to clinician-performed ultrasound.Sixty-nine EM residents completed the quantitative survey, a response rate of 85% of all current EM residents in Colombia; 52% of resident respondents had previously used ultrasound during their training. Of these, 58% indicated that they had performed <10 scans and 17% reported >40 scans. The most frequently used applications indicated by respondents were trauma, obstetrics, and procedures including vascular access. A quarter indicated they had previously received some ultrasound training, but almost all expressed an interest in learning more. Significant barriers included lack of trained teachers (indicated by 78% of respondents), absence of machines (57%), and limited time (41%). In FGDs, the barriers identified were inter-specialty conflicts over the control of ultrasonography, both institutionally and nationally, and program-specific curriculum decisions to include POC ultrasound.While currently limited in their access, EM residents in Colombia have a strong interest in integrating POC ultrasound into their training. Current barriers to expanded use include traditional barriers such as a lack of equipment seen in many developing countries, as well as inter-specialty conflicts typical of developed countries. Further collaboration is underway to help overcome these obstacles and integrate POC ultrasound into Colombian EM residency training.
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 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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".