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Record W3010964010

Potential Implications of Advanced Practice Radiography on the Canadian Healthcare System from Technologists Perspectives

2019· article· en· W3010964010 on OpenAlexaffabout
Marienell Talla, Bharvita Nakum, Sarah Abdul-Jalil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsMichener Institute
Fundersnot available
KeywordsCertificationHealth careScope (computer science)Scope of practiceVariety (cybernetics)Patient careHealthcare systemMedical educationMedicineNursingComputer scienceManagementPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Advanced practice in healthcare has emerged as a meaningful way to optimize resources and improve the patient experience. Published studies from other countries have shown that the advancement of the radiographer’s role has been successful in a variety of healthcare systems. While the Canadian Association of Medical Radiation Technologists developed a framework for the advanced practice of technologists, formal training and certification for an advanced radiographer role has yet to be introduced within the Canadian healthcare system. Despite this, many technologists currently perform delegated tasks beyond the scope of their described role. Understanding more about these delegated tasks can shed light on potential implications of a formal advanced practice radiographer role in Canada. When interviewed, many technologists find that on-site training for performing these procedures is sufficient and they were able to observe improved patient care in the department. In conclusion, this paper is written to reiterate the evidence supporting the development of an Advanced Practice role for Radiological Technologists, as well as present the perspective of technologists who perform procedures outside of a conventional technologist role.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.797
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.307
Teacher spread0.293 · 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.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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