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Record W1988203987 · doi:10.14740/jocmr1801w

Knowledge Levels Regarding Crimean-Congo Hemorrhagic Fever Among Emergency Healthcare Workers in an Endemic Region

2014· article· en· W1988203987 on OpenAlexvenueno aff
Sadiye Yolcu

Bibliographic record

VenueJournal of Clinical Medicine Research · 2014
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
Fundersnot available
KeywordsCrimean–Congo hemorrhagic feverMedicinePsychological interventionHealth careEmergency medicineEmergency departmentMedical emergencyFamily medicineDiseaseInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: In this study, we aimed to determine knowledge levels regarding Crimean-Congo hemorrhagic fever (CCHF) among emergency healthcare workers (HCWs) in an endemic region. METHODS: A questionnaire form consisting of questions about CCHF was applied to the participants. RESULTS: The mean age was 29.6 ± 6.5 years (range 19 - 45). Fifty-four (49.5%) participants were physicians, 39 (35.8%) were nurses and 16 (14.7%) were paramedics. All of the participants were aware of CCHF, and 48 (44%) of them had previously followed CCHF patients. Rates of the use of protective equipment (masks and gloves) during interventions for patients who were admitted to the emergency service with active hemorrhage were 100% among paramedics, 76.9% among nurses and 61.1% among physicians (P = 0.003). Among 86 (78.9%) HCWs who believed that their knowledge regarding CCHF was adequate, 62 (56.9%) declared that they would prefer not to care for patients with CCHF (P = 0.608). CONCLUSIONS: The use of techniques to prevent transmission of this disease, including gloves, face masks, face visors and box coats, should be explained to emergency room HCWs, and encouragement should be provided for using these techniques.

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)0.020
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.501
GPT teacher head0.596
Teacher spread0.095 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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