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Record W3206794578 · doi:10.4103/cjrm.cjrm_65_20

Building point-of-care ultrasound capacity in rural emergency departments: An educational innovation

2021· article· en· W3206794578 on OpenAlexaffvenue
Kathryn T. Young, Nicole Moon, Tandi Wilkinson

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMedical educationNursingRural areaFamily medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Point-of-care ultrasound (POCUS) use is the standard of care in emergency medicine (EM), but rural physicians face barriers to obtaining and retaining this skill and cite low confidence in their use of POCUS. Without access to high-quality educational opportunities, this important clinical tool may not be used to its full potential in rural hospitals. The Hands-On Ultrasound Education (HOUSE) programme, launched in 2015 by the University of British Columbia's (BC) Division of Rural Continuing Professional Development, is a rurally focused POCUS training and education programme that travels to rural and remote communities and aims to build a rural POCUS community of practice within BC. In this study, we present and evaluate the HOUSE programme. METHODS: year of the programme to assess participant experience and programme outcomes. RESULTS: Results from 52 semi-structured interviews indicate that there is a significant increase in self-reported confidence on specific POCUS applications and increased POCUS use after completion of the course, and we report positive experiences with the HOUSE programme. CONCLUSION: By providing a customizable, accessible, hands-on training opportunity, the HOUSE programme removes barriers to POCUS training and education for physicians in rural and remote BC. The rurally focused elements have contributed to education for rural participants that demonstrates increased confidence and the use of POCUS as a clinical tool.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.050
GPT teacher head0.355
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes2
Has abstractyes

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