Rural family physician use of point-of-care ultrasonography: experiences of primary care providers in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: In British Columbia (BC), rural and remote areas lack proximal access to radiographic services. Poor access to radiographic services in rural settings presents a challenge to timely diagnosis and screening across many disease states and healthy pregnancies. As a solution to the lack of access to radiographic services in rural settings, the Rural Coordination Centre of BC (RCCbc) supported rural Family Physicians (FPs) wishing to use PoCUS through the Intelligent Network for PoCUS (IN PoCUS) program. This study evaluates FPs' experience and use of PoCUS in their clinical practice. METHODS: This qualitative study conducted in-depth virtual interviews with 21 FPs across rural BC. The interview asked participants' motivation to participate in the RCCbc program, the type of training they received, their current use of PoCUS, their experience with the technology, and their experience interacting with specialists in regional centres. Thematic analysis of findings was undertaken. RESULTS: This study used Rogers' framework on the five elements of diffusion of innovation to understand the factors that impede and enable the adoption of PoCUS in rural practice. Rural FPs in this study differentiated PoCUS from formal imaging done by specialists. The adoption of PoCUS was viewed as an extension of physical exams and was compatible with their values of providing generalist care. This study found that the use of PoCUS provided additional information that led to better clinical decision-making for triage and allowed FPs to determine the urgency for patient referral and transport to tertiary hospitals. FPs also reported an increase in job satisfaction with PoCUS use. Some barriers to using PoCUS included the time needed to be acquainted with the technology and learning how to integrate it into their clinical flow in a seamless manner. CONCLUSION: This study has demonstrated the importance of PoCUS in improving patient care and facilitating timely diagnosis and treatment. As the use of PoCUS among FPs is relatively new in Canada, larger infrastructure support such as improving billing structures, long-term subsidies, educational opportunities, and a quality improvement framework is needed to support the use of PoCUS among rural FPs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".