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Record W2621113235 · doi:10.37341/jkg.v1i1.14

Hubungan Tingkat Pengetahuan dan Sikap Perawat Tentang Infeksi Nosokomial (INOS) Dengan Perilaku Pencegahan INOS Di Ruang Bedah RSUD DR. Moewardi Surakarta

2016· article· en· W2621113235 on OpenAlexaff
Dwi Sulistyowati

Bibliographic record

VenueJKG (JURNAL KEPERAWATAN GLOBAL) · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicineProbability samplingSpearman's rank correlation coefficientRank correlationFamily medicineNursingEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Abstract: Knowledge, Attitudes, Behaviors, Inos, Nurse. Nosocomial infections (inos) is an infection acquired during patient care in hospitals, infection is not in pain at the time of hospital admission and the patient is not within an incubation period of infectious diseases. The study aims to determine the relationship between the level of knowledge and attitudes to the behavior of nurses regarding the prevention of inos in the operating room of RSUD Dr. Moewardi Surakarta. Design used in this study was descriptive correlational cross-sectional approach. Sampling was carried out with total non-probability sampling, sample size of 30 respondents. There is a relationship between the level of the nurse’s knowledge about preventive behaviors inos with a probalility value of Spearman’s Rank correlation test for p = 0,024 is smaller than the probability p = 0,05. There is no relationship between nurse attitudes about preventive behaviors inos with a probability value of Spearman's Rank correlation test for p = 0.759 greater than the probability p = 0.05. There is a relationship between the nurse's knowledge about preventive behavior with inos. There is no relationship between attitudes to the behavior of nurses regarding the prevention of inos.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.072
GPT teacher head0.423
Teacher spread0.351 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes1
Has abstractyes

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