Point-of-Care Ultrasound Training: An Assessment of Interns’ Needs and Barriers to Training
Bibliographic record
Abstract
Background and objective The use of point-of-care ultrasound (POCUS) is generally on the rise worldwide. However, as the epidemiology of diseases and the approach to their management vary internationally, POCUS may not be universally applicable. The resources available for medical education are generally limited. Thus, when considering the development of a training program during the internship year, we sought to determine interns' perceptions of the applicability of POCUS to clinical practice, the current skill gaps, and barriers to training. Methods A validated questionnaire was distributed to the interns of the College of Medicine, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh to determine their proficiency in POCUS, and their opinions on its applicability on a 5-point Likert scale. Each skill gap was calculated by subtracting self-reported proficiency in POCUS from its perceived applicability. Results Of the 300 total interns (male: 200, female: 100), 229 participated [response rate: 76%; male: 136 (68%), female: 93 (93%)]. The use of POCUS to detect abdominal free fluid was perceived to be the most applicable use (mean: 3.9 ±1.1); scanning for consolidation was the least applicable (mean: 3.0 ±1.2). Knowledge and proficiency among the sample were generally poor. The skill gap was greatest for the assessment of inferior vena cava collapsibility (mean: 1.4 ±1.3) and least for the identification of pneumothorax (mean: 0.5 ±1.5). Although three-quarters of the participants (170) agreed that POCUS was an essential skill, 36 (16%) stated that they had no interest in it, and nearly half (101) believed that they did not have time to learn POCUS. Conclusions While POCUS is applicable to medical interns in Saudi Arabia, significant skill gaps exist. However, our sample's perception of the applicability of POCUS was less favorable than that of internal medicine (IM) residents in Canada. Thus, initiating POCUS training during the internship year may yield suboptimal results. Interns must prioritize medical licensing examinations and applications for residency training. Indeed, many interns believe that they do not have enough time to learn POCUS. Thus, prioritizing the training of residents in POCUS may be a more effective use of the finite resources available for medical education.
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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 | 0.004 | 0.011 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".