Use of point-of-care ultrasound in rural British Columbia
Bibliographic record
Abstract
OBJECTIVE: To determine the scale and scope of use of point-of-care ultrasound (POCUS) in rural British Columbia (BC). DESIGN: Online survey. SETTING: Rural BC. PARTICIPANTS: Physicians practising in rural BC communities. MAIN OUTCOME MEASURES: Practitioner demographic and practice characteristics, locations and frequency of POCUS use, POCUS education and training, and practitioner attitudes about and barriers to POCUS use. RESULTS: Two hundred twenty-seven surveys were completed in fall 2021, corresponding to a response rate of 11.9% of all rural practitioners in BC. A total of 52.1% of respondents worked in communities with less than 10,000 people, while 24.9% had practices with relatively large proportions of Indigenous patients (more than 20% of the practice population). Respondents reported ease of access to local POCUS devices, with use highest in emergency departments (87.2%) followed by ambulatory care clinic (54.7%) and inpatient (50.3%) settings. Use of POCUS influenced clinical decision making in half the occasions in which it was employed, including a range of diagnostic and procedural applications. Barriers to use included lack of training, limited time to perform POCUS scans, and absence of image review or consultative support. Needed support for POCUS identified by respondents included real-time image acquisition advice and funding for both device acquisition and training. Recommendations for including POCUS training in undergraduate and residency education were strongly supported. CONCLUSION: Use of POCUS in BC is expanding in frequency, scope, and scale in practices serving rural areas and in rural communities with large Indigenous populations, with practitioners reporting important improvements in clinical care as a result. Future research could help improve systemic support for POCUS use, guide needed curriculum changes in medical school and postgraduate training, and be used to inform continuing professional development needs.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".