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Record W2991261828 · doi:10.24908/pocus.v4i2.13691

Perceived Barriers and Facilitators to the use of Point-of-Care Ultrasound for Clinicians in Oregon

2019· article· en· W2991261828 on OpenAlexvenueno aff
Camellia Dalai, Renee K. Dversdal

Bibliographic record

VenuePOCUS Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPoint of care ultrasoundMedicineClinical PracticePhysical examinationPoint of careResource (disambiguation)PerceptionNursingFamily medicinePsychologySurgeryEmergency department

Abstract

fetched live from OpenAlex

The use of Point-of-Care Ultrasound (POCUS) to provide clinical data beyond the history and physical examination is a relatively new practice for primary care providers and hospitalists. It takes many hours of dedicated ultrasound (US) training and practice to achieve POCUS proficiency; further, perceptions and attitudes of clinicians play a major role in adopting POCUS into daily clinical repertoire [1, 2]. Thus there are many possible barriers that could impede a clinician’s ability to develop the skillset to use POCUS in clinical practice. The state of Oregon encompasses vast rural and underserved areas where POCUS could be a useful resource to improve local patient care [3,4]. For this reason, a qualitative survey study was conducted to assess the perceived barriers of clinicians to the clinical incorporation of POCUS.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.045
GPT teacher head0.347
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2019
Admission routes1
Has abstractyes

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