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Record W4376612290 · doi:10.3390/ijerph20105821

Barriers and Facilitators to Point-of-Care Ultrasound Use in Rural Australia

2023· article· en· W4376612290 on OpenAlexaff
Annie C. Arnold, Richard Fleet, David Lim

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversité Laval
FundersFlinders University
KeywordsQuality assuranceHealth careQuality (philosophy)MedicineNursingRural areaPoint of careTelemedicineQualitative researchMedical educationBusinessPolitical science

Abstract

fetched live from OpenAlex

This study explores the barriers and facilitators to point-of-care ultrasound (POCUS) use and adoption in rural healthcare since POCUS is a useful resource for rural clinicians to overcome the challenges associated with limited on-site clinical support, such as limited diagnostic imaging services and infrastructure. A qualitative descriptive study was employed, interviews with ten rural clinicians were conducted, and the data were analysed using the Walt and Gilson health policy framework to guide interpretation. Barriers include a lack of standardised training requirements, the cost of the devices and challenges recouping the costs of purchase and training, difficulty with the maintenance of skills, and a lack of an effective method to achieve quality assurance. Coupling POCUS with telemedicine could address the issues of the maintenance of skills and quality assurance to facilitate increased POCUS use, leading to positive patient safety and social and economic implications.

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)0.006
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.132
GPT teacher head0.462
Teacher spread0.330 · 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 designQualitative
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

Citations55
Published2023
Admission routes1
Has abstractyes

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