Barriers to learning and using point-of-care ultrasound: a survey of practicing internists in six North American institutions
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (POCUS) is increasingly used in internal medicine, but a lack of trained faculty continues to limit the spread of POCUS education. Using a framework based on organizational change theories, this study sought to identify barriers and enablers for hospital-based practicing internists to learn and use POCUS in clinical practice. METHODS: We invited practicing internists at six North American institutions to participate in an electronic survey on their opinions regarding 39 barriers and enablers. RESULTS: Of the 342 participants invited, 170 participated (response rate 49.3%). The top barriers were lack of training (79%), lack of handheld ultrasound devices (78%), lack of direct supervision (65%), lack of time to perform POCUS during rounds (65%), and lack of quality assurance processes (53%). The majority of participants (55%) disagreed or strongly disagreed with the statement "My institution provides funding for POCUS training." In general, participants' attitudes towards POCUS were favourable, and future career opportunities and the potential for billing were not considered significant factors by our participants in the decision to learn or use POCUS. CONCLUSIONS: This survey confirms the perceived importance of POCUS to practicing internists. To assist in closing faculty development gap, interventions should address training, supervision, quality assurance processes, availability of handheld devices, as well as dedicated time to perform POCUS during clinical care.
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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 | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".