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Record W2209209971 · doi:10.1155/2015/736175

Knowledge, Practice, and Associated Factors towards Prevention of Surgical Site Infection among Nurses Working in Amhara Regional State Referral Hospitals, Northwest Ethiopia

2015· article· en· W2209209971 on OpenAlexfundno aff
Freahiywot Aklew Teshager, Eshetu Haileselassie Engeda, Workie Zemene Worku

Bibliographic record

VenueSurgery Research and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsnot available
FundersUniversity of GondarUniversity of WashingtonUniversity of TorontoMassachusetts General HospitalUniversity of the PhilippinesImperial College Healthcare NHS TrustImperial College LondonBrigham and Women's Hospital
KeywordsReferralMedicineFamily medicineNursingKnowledge levelPsychology

Abstract

fetched live from OpenAlex

Knowledge and practice of nurses about surgical site infections (SSIs) are not well studied in Ethiopia. This paper contains findings about Northwest Ethiopian nurses' knowledge and practice regarding the prevention of SSIs. The main objective of the study was to assess knowledge, practice, and associated factors of nurses towards the prevention of SSIs. The study was done using a questionnaire survey on randomly selected 423 nurses who were working in referral hospitals during the study period. The study showed that more than half of the nurses who participated in the survey had inadequate knowledge about the prevention of SSIs. Moreover, more than half of them were practicing inappropriately. The most important associated factors include lack of training on evidence based guidelines and sociodemographic variables (age, year of service, educational status, etc.). Training of nurses with the up-to-date SSIs guidelines is recommended.

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)0.012
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.171
GPT teacher head0.443
Teacher spread0.272 · 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.

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

Citations79
Published2015
Admission routes1
Has abstractyes

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