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Record W2000940638 · doi:10.2202/1548-923x.1635

Keeping the Vision: Sustaining Social Consciousness with Nursing Students following International Learning Experiences

2009· review· en· W2000940638 on OpenAlexaffabout
Sheryl Reimer‐Kirkham, Lynn Van Hofwegen, Darlane Pankratz

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2009
Typereview
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsTrinity Western University
Fundersnot available
KeywordsConsciousnessSocial consciousnessGlobalizationCitizen journalismPsychologyNursingIntentionalityPublic relationsMedical educationPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

In an era of globalization, increasing numbers of nursing programs are initiating international learning experiences, yet empirical data is lacking regarding long term benefits and effects of international placements. This paper presents findings from a participatory action study designed in response to this gap. Objectives were to describe student learning in international experiences, and to facilitate strategies that supported integration of this learning into personal and professional domains upon return to Canada. Seventeen students and three faculty participated over twelve months following their international experiences. Initial responses to the international experience included reports of new ways of viewing the world - often characterized by heightened social consciousness - yet in the immediate and longer-term many struggled with how to translate and sustain this learning in home settings. Considerable effort and intentionality was required to sustain social consciousness over time. Based on these findings, a preliminary framework for international experiences is presented.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.109
GPT teacher head0.519
Teacher spread0.410 · 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
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

Citations51
Published2009
Admission routes2
Has abstractyes

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