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Record W2991388293 · doi:10.21608/asnj.2019.61136

Hand Skin Problems: Prevalence and Risk Factors Among Nurses Working at Surgical Departments in Ministry of Health Hospitals

2019· article· en· W2991388293 on OpenAlexaboutno aff
Hebatullah Fargly

Bibliographic record

VenueAssiut Scientific Nursing Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministryMedicineGovernment (linguistics)Health careNursingFamily medicineSample (material)Quarter (Canadian coin)Cross-sectional study

Abstract

fetched live from OpenAlex

Background: Hand skin problems are widespread problems among health care members especially nurses.Aims: to determine the prevalence and risk factors of hand skin problems among nurses who are working at surgical departments in Ministry of Health Hospitals. Design: Descriptive cross-sectional study. Setting: This study was carried out at three ministry health hospitals in Assiut government: Al-Eman, Al-Shamlaa and Ophthalmology Hospitals. Sample: A purposive sample of 200 nurses working in surgical and operating room departments at Assiut ministry of health hospitals. Tool: A structured self-administrator’s questionnaire, included demographic characteristic of nurses, history about hand skin problems and risk factors for hand skin problems. Results: More than one third of nurses working from 40-45 hours weekly. More than one quarter of nurses were having hand skin problems and 11.5 % were complaining from hand skin problems less than one year ago. Majority of nurses had a satisfactory level of knowledge about risk factors of hand skin problems. Repeated hand washing is most common risk factors of hand skin problem. Conclusion and Recommendations: About aquarter of nurses have hand skin problems and some of them complained from hand skin problems less than one year ago. Efforts to improve skin condition must focus on improving products and identifying any interactive effects between hand care products and glove materials and brands.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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