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Record W2142201308

El aprendizaje de la Nueva Cultura del Agua usando TICs por los estudiantes de Enfermería Comunitaria de la Universidad de Zaragoza

2008· article· es· W2142201308 on OpenAlexaboutno aff
Concepción Germán Bes, Ana Gràcia, Victoria Arbones Cobos

Bibliographic record

VenueMedicina naturista · 2008
Typearticle
Languagees
FieldHealth Professions
TopicNursing care and research
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationCompetence (human resources)PsychologyLibrary scienceSociologyHumanitiesComputer scienceSocial psychologyArt
DOInot available

Abstract

fetched live from OpenAlex

Abstract: A course on Community Nursing is taught at our university in the 3 rd and last year of the BA in Nursing. For the last six years we have been teaching an active method of information finding in the modules devoted to Health Promotion and Environmental Health. The cognitive approach to teaching and learning proposes a number of strategies such as ‘learning through discovery’ and ‘cooperative work’ (Ausubel). In the century of information and using the Information and Communication Technologies, our purpose is that the student will carry out an active information search. In this paper we analyse the results of documentation on water for human consumption in three different media: scientific papers, popular papers in regional or national printed media, and reviews. The purpose is for the students to acquire a sound competence in searching, critically reading and writing bibliographical entries for documents (Vancouver norms). But mainly that they will be able to combine the cognitive scientific data with the emotions aroused by the literature.

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)0.005
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.446
Teacher spread0.416 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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