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Record W2090570820 · doi:10.2202/1548-923x.1166

Narratives of Social Justice: Learning in Innovative Clinical Settings

2005· article· en· W2090570820 on OpenAlexaff
Sheryl Reimer‐Kirkham, Lynn Van Hofwegen, Catherine Hoe Harwood

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsTrinity Western University
Fundersnot available
KeywordsCognitive dissonanceTransformative learningWitnessCurriculumSociologyNarrativePedagogyPoliticsNursingFocus groupService-learningPsychologyPolitical scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

The nursing profession has renewed its commitment to social and political mandates, resulting in increasing attention to issues pertaining to diversity, vulnerable populations, social determinants of health, advocacy and activism, and social justice in nursing curricula. Narratives from a qualitative study examining undergraduate nursing student learning in five innovative clinical settings (corrections, international, parish, rural, and aboriginal) resonate with these curricular emphases. Data were derived from focus groups and interviews with 65 undergraduate nursing students, clinical instructors, and RN mentors. Findings of this study reveal how students in innovative clinical placements bear witness to poverty, inequities, and marginalization (critical awareness), often resulting in dissonance and soul-searching (critical engagement), and a renewed commitment to social transformation (social change). These findings suggest the potential for transformative learning in these settings.

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)0.017
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.026
Scholarly communication0.0090.007
Open science0.0020.016
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.130
GPT teacher head0.546
Teacher spread0.415 · 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 designQualitative
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

Citations54
Published2005
Admission routes1
Has abstractyes

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