Start spreading the news: a deliberate approach to POCUS program development and implementation
Bibliographic record
Abstract
While there is an expanding body of literature on Point-of-Care Ultrasound (POCUS) pedagogy, administrative elements that are necessary for the widespread adoption of POCUS in the clinical environment have received little attention. In this short communication, we seek to address this gap by sharing our institutional experience with POCUS program development and implementation. The five pillars of our program, selected to tackle local barriers to POCUS uptake, are education, workflow, patient safety, research, and sustainability. Our program logic model outlines the inputs, activities, and outputs of our program. Finally, key indicators for the monitoring of program implementation efforts are presented. Though designed for our local context, this approach may readily be adapted toward other clinical environments. We encourage others leading the integration of POCUS at their centers to adopt this approach not only to achieve sustainable change but also to ensure that quality safeguards are in place.
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 | 0.169 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".