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Record W2735033714 · doi:10.26550/2209-1092.1015

A selected international appraisal of the role of the Non-Medical Surgical Assistant

2017· article· en· W2735033714 on OpenAlexaboutno aff
Toni Hains, Haakan Strand, Catherine Turner

Bibliographic record

VenueJournal of Perioperative Nursing · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsnot available
FundersGoddard Space Flight Center
KeywordsSurgical proceduresMedicineMedical educationSurgery

Abstract

fetched live from OpenAlex

The term Non-Medical Surgical Assistant (NMSA) is not widely acknowledged in Australia but is used to describe the role of clinicians without a medical degree or qualification who provide clinical services during the perioperative phase of a patient’s journey. The role of NMSA has many configurations internationally and not all NMSA roles arise from a nursing platform. To date, the implementation of many Advanced Practice Nurse (APN) roles have lacked educational support or professional direction. The literature supports thestandardisation of APN roles where they are regulated by the profession and attained through an appropriate tertiary level qualification. In this paper, we review characteristics of the roles of the NMSAs in the United States of America, Canada, the United Kingdom and New Zealand, countries that have similar standards for practice to Australia and provide a similar standard of health care. We will discuss implications for perioperative nurses and make recommendations for a future approach which formalises the role of the NMSA for the Australian context.

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)0.008
metaresearch head score (Gemma)0.016
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.456
Teacher spread0.429 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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