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Record W3080433630 · doi:10.5430/jnep.v10n12p14

Development of latex wound models for the wound dressing training of nursing students

2020· article· en· W3080433630 on OpenAlexvenueno aff
Benyaporn Bannaasan

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typearticle
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsnot available
Fundersnot available
KeywordsWound dressingWilcoxon signed-rank testMedicineNursingPurchasingOperations managementEngineeringMann–Whitney U testMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

A model is an essential instrument for the practical training of nursing students before field training in the ward, and an aid which creates skill and confidence for the students. The objective of this research is to develop a latex wound model for the wound dressing training of nursing students. Three research procedures are 1) to study documents and data relating to the development of latex wound models, 2) to construct the latex wound models and the research instruments, and 3) to try out the latex wound models, and evaluate the latex wound models efficiency. Participants are the 60 second-year nursing students. A wet dressing type latex wound model efficiency evaluation form and a dry dressing type latex wound model efficiency evaluation form were used for data collection. A reliability of 0.884 and 0.889 was acquired. The data were analyzed using descriptive statistics and Wilcoxon Signed Ranks Test. The finding of the studying results indicated that the mean scores of efficiency of both invented wet dressing type and dry dressing type latex wound model were higher than that of the original wound model of the Faculty of Nursing at statistical significance (p < .05). The finding indicated that the latex wound model had higher quality than those of the original wound model. Also, it helps the Faculty of Nursing save budget on purchasing expensive models.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.604
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.235
GPT teacher head0.453
Teacher spread0.218 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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