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Record W2796228853 · doi:10.1111/nup.12209

Nursing knowledge: A middle ground exploration

2018· article· en· W2796228853 on OpenAlexaff
Mariko Sakamoto

Bibliographic record

VenueNursing Philosophy · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAssertionDiversity (politics)EpistemologyNursing researchIdeologySociologyField (mathematics)Space (punctuation)Body of knowledgeCommon groundHealth careNursingMedicineComputer sciencePolitical scienceLawPhilosophyLinguisticsCommunication

Abstract

fetched live from OpenAlex

The discipline of nursing has long maintained that is has a unique contribution to make within the health care arena. This assertion of uniqueness lies in great part in the discipline's claim to a distinct body of knowledge. Nursing knowledge is characterized by diverse and multiple forms of knowing and underpins the work of all nurses, regardless of field of practice. Unfortunately, it has been challenging for the discipline to take full ownership of its epistemological diversity, largely due to factors such as competing worldviews, and ideological and binary positioning. A philosophical middle ground stance is proposed as a way for the discipline to contemplate, discuss and develop nursing knowledge; a middle space that provides the freedom to consider competing worldviews while still allowing for the discipline to fully express itself in all of its epistemological diversity. In being able to enact its multiple forms of knowledge in a creative and open space that is open to different ideas and worldviews, not only can nursing take full ownership of its practice and its unique knowledge, it can also demonstrate how best to navigate an increasingly polarized world. In a world that is increasingly fixated on binary solutions and dualistic points of view, it is time for nursing to celebrate its epistemological diversity.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.087
GPT teacher head0.359
Teacher spread0.272 · 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.

Study designTheoretical or conceptual
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

Citations11
Published2018
Admission routes1
Has abstractyes

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