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Record W4410266380 · doi:10.1111/jan.17042

Operationalising Anti‐Oppression in Doctoral Nursing Education

2025· review· en· W4410266380 on OpenAlexaff
Katerina Melino, Samantha Louie‐Poon

Bibliographic record

VenueJournal of Advanced Nursing · 2025
Typereview
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOppressionPedagogySociologyCritical pedagogyNurse educationEmpowermentNursingMedicinePolitical sciencePolitics

Abstract

fetched live from OpenAlex

AIMS: Outline the rationale, experiences and vision for a progressive pedagogy in nursing doctoral education that embraces collaboration and collective world-building as strategies for developing liberatory knowledge. DESIGN: Conceptual exploration and vision statement grounded in the pedagogical theories of hooks and Freire. METHODS: Theoretical analysis of emancipatory pedagogies, review of existing literature on collaboration in doctoral programs and a reflective account. The framework proposed is informed by bell hooks' engaged pedagogy and Freire's critical pedagogy. RESULTS: The paper identifies the limitations of traditional, individualistic approaches in doctoral nursing programs and proposes a re-envisioned pedagogical framework that emphasises community and collective inquiry. CONCLUSION: The proposed progressive pedagogy for doctoral nursing education seeks to operationalise anti-oppression and decolonisation by fostering a collaborative and community-based approach to knowledge production. IMPLICATIONS FOR THE PROFESSION: Implementing this progressive pedagogical framework in doctoral nursing education can lead to the development of researchers who are better equipped to address social problems such as racism, oppression and health inequity. REPORTING METHOD: EQUATOR guidelines are not applicable. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct or reporting.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.513
Teacher spread0.432 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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