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Record W4365396418 · doi:10.5114/lr.2023.126304

Cultural aspects of wound care

2023· article· en· W4365396418 on OpenAlexaboutno aff
Piotr Wojda

Bibliographic record

VenueLeczenie Ran · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicPolish-Jewish Holocaust Memory Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWound careMedicineSurgery

Abstract

fetched live from OpenAlex

The increase in migration increases the likelihood of caring for a patient from a different culture. Cultural and religious factors may determine the wound healing process. The aim of the study was familiarize wound care nurses with patients' cultural and religious beliefs that may affect the wound healing process. Material and methods: An online database was searched using the PubMed and Google search engines. Then, the available literature was analysed that met the conditions for inclusion in the study of the subject of the article, and the selected content was used in the work. Results: Cultural and religious factors have a significant impact on the process of wound formation and the course of treatment (nutrition restrictions, selection of dressings, superstitions, the use of alternative methods of treatment). It should be emphasized that spiritual leaders consent to the use of medicinal products prohibited by religion, with the informed consent of the patient, in order to save human health Conclusions: Nurses should be aware of their patients' cultural beliefs about wounds and incorporate them into the wound management process.

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)0.014
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: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.006
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0310.009

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 head0.058
GPT teacher head0.274
Teacher spread0.216 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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