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Record W2067352588 · doi:10.3928/01484834-20140821-23

Baccalaureate Nursing Students’ Experience of Dyadic Learning in an Acute Care Setting

2014· article· en· W2067352588 on OpenAlexaboutno aff
Gregg Trueman, Joseph Osuji, Mohamed Toufic El Hussein

Bibliographic record

VenueJournal of Nursing Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsPraxisAutonomyNursingPsychologyMedical educationEmpowermentUnit (ring theory)Medicine

Abstract

fetched live from OpenAlex

This article describes a unique learning project designed to address the praxis gap between baccalaureate nursing students' clinical learning and theoretic principles of collaborative practice on an acute medical-surgical unit in Canada. The study was framed by the active engagement model to provide second-year nursing students a nontraditional approach to develop their nursing practice. Clinical faculty partnered with medical-surgical nursing staff and eight baccalaureate nursing students to explore the experience of collaborative learning and stakeholders' anticipated learning outcomes while working in dyads. A modified phenomenological approach was used in understanding the experience of dyadic learning through reflective journals, course evaluation data, and a semistructured exit interview for analysis. Four themes were revealed based on students' reflection of their experience: work engagement, relational practice, autonomy, and empowerment. These themes underscore the strengths and opportunities associated with this nontraditional approach to clinical learning.

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)0.007
metaresearch head score (Gemma)0.015
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 score0.012
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0080.004
Open science0.0020.015
Research integrity0.0020.005
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 head0.031
GPT teacher head0.522
Teacher spread0.491 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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