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Interprofessional Education and Practice Implications of the Institute of Medicine Future of Nursing Report

2011· article· en· W38581961 on OpenAlexfundno aff
Mary Lou Manning, Beth Ann Swan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNursingMedicineMedical educationNursing practiceInterprofessional educationHealth carePolitical science

Abstract

fetched live from OpenAlex

The Future of Nursing: Leading Change, Advancing Health, released by the Institute of Medicine (IOM) on October 5, 2010, is the latest in a series of reports that have had a significant impact on creating positive change to improve health care in the United States. The report provides a vision of a future health care system that would make high-quality care accessible to all, promote wellness and disease prevention, reliably improve health outcomes, and provide compassionate care throughout a person’s lifespan. This report, in concert with the provisions of the Affordable Care Act, must act as a catalyst for all health professions to find significant opportunities for collaboration and the impetus to play to each of our strengths to advance quality health care for all citizens.

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)0.014
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0070.004
Open science0.0020.012
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0500.005

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 head0.099
GPT teacher head0.484
Teacher spread0.386 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2011
Admission routes1
Has abstractyes

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