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Record W2151939249 · doi:10.5539/gjhs.v5n1p203

Exploring the Competency of the Jordanian Intensive Care Nurses towards Endotracheal Tube and Oral Care Practices for Mechanically Ventilated Patients: An Observational Study

2012· article· en· W2151939249 on OpenAlexvenueno aff
Abdul‐Monim Batiha, Ibrahim Bashaireh, Mohammed ALBashtawy, Sami Shennaq

Bibliographic record

VenueGlobal Journal of Health Science · 2012
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObservational studyIntensive careEndotracheal tubeCritical care nursingNursingRespiratory careIntensive care medicineHealth careIntubationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Oral care is an important feature of nursing; it is known that oropharynx is considered the main reservoir of bacterial colonization, so the removal of oral infection is a major duty of all health care providers, particularly nurses. We performed this study to explore endotracheal tube and oral care practices for mechanically ventilated patients of Jordanian intensive care nurses, and to study Jordanian intensive care nurses' practices during, prior to, and post endotracheal tube and oral care for mechanically ventilated patients. Endotracheal tube and oral care of Jordanian intensive care nurses for mechanically ventilated patients was compared with recommendations for endotracheal tube and oral care of American Association of Critical Care Nurses and guidelines of Centers for Disease Control and Prevention. Non- participant structured observational design was conducted using a 24 -item structured observational schedule. The findings show that nurses different in their oral care practices; did not follow American Association of Critical Care Nurses recommendations; and therefore delivered lower-quality oral care than predictable. Important inconsistencies were observed in the nurses' hyperoxygenation, respiratory assessment techniques and infection control practices.

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)0.002
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 score0.051
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.285
GPT teacher head0.459
Teacher spread0.174 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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