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Record W4244221120 · doi:10.18192/aporia.v8i2.2789

[no title]

2016· article· fr· W4244221120 on OpenAlexvenueaboutno aff
Annie Rioux‐Dubois, Amélie Perron

Bibliographic record

VenueAporia · 2016
Typearticle
Languagefr
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsCLARITYScope (computer science)Health careScope of practiceProcess (computing)NursingPsychologyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Nurse Practitioners (NPs) are clinically effective and safe. They positively influence patient outcomes, and they increase access to care while decreasing health care costs. Despite these significant benefits, NPs can seldom practice to their full scope and often experience interprofessional tensions. The supposed lack of clarity around NPs’ role is often cited as a barrier to seamless integration, despite clear legal and professional delineation. We suggest other factors are at play within the Canadian health care system that explain why, after almost four decades, NPs’ full involvement as equal health care partners and their job satisfaction remain modest at best. New, critical frameworks are needed to uncover the various contingencies that mediate their integration process. This paper explores how Actor-Network Theory (ANT) can provide such a framework to analyze contemporary issues in advanced nursing practice. ANT’s main concepts are explored along with their applicability to an examination of NPs’ integration in the Canadian health care system.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.975
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.004
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.004

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 head0.033
GPT teacher head0.444
Teacher spread0.412 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes2
Has abstractyes

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