MétaCan
Menu
← Back to cohort
Record W3094299040 · doi:10.5539/gjhs.v12n13p19

Knowledge and Compliance Levels Regarding Hand Hygiene Practices among Nurses in the Neonatal Intensive Care Unit (NICU) of the Princess Margaret Hospital, Nassau, Bahamas

2020· article· en· W3094299040 on OpenAlexvenueno aff
Dorothea Francis, Philip Onuoha, Esther Shirley Daniel, Virginia Víctor

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsHygieneMedicineNeonatal intensive care unitDemographicsCompliance (psychology)Family medicineKnowledge levelIntensive care unitCross-sectional studyNursingPediatricsDemographyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to assess knowledge and compliance levels of hand hygiene among registered nurses at the Princess Margaret Hospital, Neonatal Intensive Care Unit (NICU), Nassau, Bahamas. METHOD: A cross-sectional survey was conducted in June 2019. A 32-item self-administered questionnaire was provided to 40 registered nurses to assess their knowledge and compliance levels to hand hygiene practices. RESULTS: All respondents were females. The results showed that 45% of the nurses had excellent knowledge, 27.5% had good knowledge on hand hygiene, while 27.5% had an average knowledge level. There was a statistically significant association between their knowledge level and their age, years of experience, length of time in the NICU and their level of education (p≤0.05). There was no statistically significant association between their compliance level and their socio-demographics (p≥0.05). CONCLUSIONS: Nurses’ knowledge levels were rated as good and so were their practice levels.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0020.000

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 head0.099
GPT teacher head0.427
Teacher spread0.328 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health Science→Same topicInjury Epidemiology and Prevention→French-language works237,207→