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Record W33096334 · doi:10.1016/j.nepr.2020.102885

Development of modular ontologies in Casl

2006· article· en· W33096334 on OpenAlexaboutno aff
Klaus Lüttich, Claudio Masolo, Stefano Borgo

Bibliographic record

VenueNurse Education in Practice · 2006
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceProgramming languageDescription logicAlgebraic specificationRotation formalisms in three dimensionsModular designWeb Ontology LanguageSpecification languageSemantic WebArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This study describes nursing students' perspectives on learning math for medication calculations in a Canadian baccalaureate nursing program in Qatar. There is a dearth of guidance within the nursing literature to support teachers to develop pedagogical methods to help improve students' math competence. Moreover, the challenge of teaching math skills to student nurses in Qatar is particularly difficult because there is little standardization of curricula in secondary education. Data collected from focus groups was analyzed using interpretive description. Focus groups included, Group 1 those students who were beginning to learn math for medication calculations and had not completed the required math module and Group 2, students who had not yet completed the math module. Themes emerging from the data included from first year students, 1. Fear of math resulting in resistance to learning math for medication administration. 2. Student success is dependent on good instructors. 3. Student resentment towards perceived 'complicated' math in the nursing program. Themes from second year students included 1. Lack of nursing student's confidence with medication calculation within the clinical settings 2. Lack of self-directedness to uptake math knowledge 3. Incongruence amongst clinical instructors with applied math practice whilst in the clinical setting.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.322
Teacher spread0.307 · 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 teacher head, 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

Citations7
Published2006
Admission routes1
Has abstractyes

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