Barriers and Facilitators to Point-of-Care Ultrasound Use in Rural Australia
Bibliographic record
Abstract
This study explores the barriers and facilitators to point-of-care ultrasound (POCUS) use and adoption in rural healthcare since POCUS is a useful resource for rural clinicians to overcome the challenges associated with limited on-site clinical support, such as limited diagnostic imaging services and infrastructure. A qualitative descriptive study was employed, interviews with ten rural clinicians were conducted, and the data were analysed using the Walt and Gilson health policy framework to guide interpretation. Barriers include a lack of standardised training requirements, the cost of the devices and challenges recouping the costs of purchase and training, difficulty with the maintenance of skills, and a lack of an effective method to achieve quality assurance. Coupling POCUS with telemedicine could address the issues of the maintenance of skills and quality assurance to facilitate increased POCUS use, leading to positive patient safety and social and economic implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".