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Record W2032642323 · doi:10.1515/ijnes-2013-0009

Knowing Self and Caring Through Service Learning

2013· article· en· W2032642323 on OpenAlexaff
Ruth Schofield, Mary Allan, Tracey Jewiss, Amy Silvestri Hunter, Nancy Sinclair, Alison Diamond, Casey Sidwell

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsConestoga CollegeMohawk CollegeMcMaster University
Fundersnot available
KeywordsCurriculumNursingNurse educationContext (archaeology)PerceptionPsychologyQualitative researchPedagogyService (business)Service-learningMedical educationMedicineSociology

Abstract

fetched live from OpenAlex

Professional caring is the essence of nursing practice. Reflection on personal assumptions and beliefs challenge stereotypic views that influence professional caring and nursing care. An innovative educational pedagogy known as service learning creates an opportunity for students to reflect on self in the context of service to others; it is through this pedagogy that personal assumptions and beliefs are challenged as students become registered nurses. A qualitative descriptive study engaged undergraduate first and second year nursing students through interviews and reflections. The purposes of this study were to describe students' perception of self and caring in service learning, any changes in the perceptions of self over time, and the connection of self to others. Results identified three major themes: understanding self, becoming a nurse and learning to care with increasing depth over the two years. Implications for nursing curriculum and further research are discussed.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.024
Scholarly communication0.0060.004
Open science0.0000.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.083
GPT teacher head0.417
Teacher spread0.334 · 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 designObservational
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

Citations27
Published2013
Admission routes1
Has abstractyes

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