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Record W2048416730 · doi:10.1515/ijnes-2012-0011

How does the Nurse Educator Measure Caring?

2013· article· en· W2048416730 on OpenAlexaff
Caroline Porr, Rylan Egan

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2013
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsQueen's UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsScholarshipNurse educationConstruct (python library)NursingIntentionalityPsychologyDimension (graph theory)Health careNurse educatorEmpathyPedagogyMedicineComputer scienceSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

The purpose of this article is to advance worldwide scholarship of nursing education by introducing a novel approach to evaluate nursing students' level of human caring. We propose an innovative tool that can be used by nurse educators to measure the construct of caring. Caring encompasses three dimensions: intentionality, relationality, and responsivity. The dimensions are drawn from theoretical, practice, and education literatures. The innovative tool, named the Caring Interaction Inventory, exposes nursing students to audio-video recordings of complex real-life healthcare encounters. Nursing students are required to choose from several options, caring behaviors that would best address holistic patient needs. Caring behaviors chosen for a given healthcare encounter and the rationale provided by the student, enable the nurse educator to evaluate the student's caring in terms of intentionality, relationality, and responsivity. The sum total of the student's performance within each dimension constitutes the student's overall caring grade.

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)0.022
metaresearch head score (Gemma)0.140
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.140
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.115
GPT teacher head0.450
Teacher spread0.335 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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Same venueInternational Journal of Nursing Education ScholarshipSame topicFamily and Patient Care in Intensive Care UnitsFrench-language works237,207