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Record W4401769519 · doi:10.1111/nup.12501

Reimagining a nursing ecosystem in an uncertain world

2024· article· en· W4401769519 on OpenAlexaff
Pawel Krol, Rochelle Einboden, Horas Wong, Lynore Geia, Agness Tembo

Bibliographic record

VenueNursing Philosophy · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of OttawaUniversité Laval
Fundersnot available
KeywordsEmpathyIndigenousPoliticsSociologyIdeologyEnvironmental ethicsEmancipationIndividualismResistance (ecology)Engineering ethicsEpistemologyPolitical sciencePsychologySocial psychologyEcologyLawPhilosophy

Abstract

fetched live from OpenAlex

The discussion paper synthesises the insights shared during a keynote panel at the 26th International Philosophy of Nursing Conference, themed "Reimagining a nursing ecosystem in an uncertain world." It delves into the substantial impact uncertainty has on nursing, offering innovative strategies for reconceptualization. Through a critical examination of evidence-based practice, the tendency to homogenise nursing is discussed, prompting advocacy for a Nietzschean political framework as a form of resistance and emancipation. Drawing inspiration from Donna Haraway, a transition from individualistic to relational approaches is needed, especially amidst social and economic instability. Furthermore, the integration of caritas and Indigenous epistemologies underscore the significance of cultural sensitivity in nursing. Meanwhile, Ubuntu philosophy champions empathy and collective values, while a convergence of Eastern and Western philosophical traditions promises to enrich nursing practice. Ultimately, the panellists advocate for overcoming rigid ideologies in favour of a pluralistic approaches, ontologies, epistemologies and ethics to address contemporary challenges, and signal for a philosophical reorientation to engage uncertainty and reshape nursing roles in an ever-evolving and world.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.007
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 head0.193
GPT teacher head0.544
Teacher spread0.351 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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