El aprendizaje de la Nueva Cultura del Agua usando TICs por los estudiantes de Enfermería Comunitaria de la Universidad de Zaragoza
Bibliographic record
Abstract
Abstract: A course on Community Nursing is taught at our university in the 3rd and last year of the BA in Nursing. For the\nlast six years we have been teaching an active method of information finding in the modules devoted to Health Promotion\nand Environmental Health. The cognitive approach to teaching and learning proposes a number of strategies such as ¿learning\nthrough 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\nable to combine the cognitive scientific data with the emotions aroused by the literature.\nMethod: During the four months in which this course takes place, students have to gather information and make critical\nreadings, which is to be written down in cards then handed over to the lecturers. Free popular newspapers were excluded\nas they lack sufficient reliability and because most pieces of news are very short. The analysis was carried out on the basis of the cards, the papers and the reviews provided by the students as part of their evaluation process.\nPreliminary: 140 in 150 provided some documentation on water, taken from scientific journals in the area of health, scientific\njournals in other areas, regional and national newspapers, regional, national and international ethnologic literature.\nDiscussion: This information, together with that provided in the classroom and in the teaching guides, are a part of the\nessay-type evaluation, in such a way as to enhance critical thinking and contrasting tendencies among scientists as well\nas among laymen as represented in newspaper articles. At first, students may resent this methodology as too new and unknown, but later on they find it extremely gratifying and useful for evaluation, as they usually get very good results. The evaluation of this course is completed by a Multiple Response Test, class papers and case study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".