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Record W2996824641 · doi:10.5539/gjhs.v12n1p149

Nurses’ and Physicians’ Attitudes Towards Nurse-Physician Collaboration in Critical Care

2019· article· en· W2996824641 on OpenAlexvenueno aff
Fatimah S. Alsallum, Maram Banakhar, Sulafah K. Gattan, Salha A. Alwalani, Roaa A. Alsuhaim, Raghad A. Samarkandi

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsNursingTeamworkHealth careMedicineScale (ratio)Family medicineMEDLINE

Abstract

fetched live from OpenAlex

Nurses-physician collaboration is crucial for patient’s safety and patient’s outcomes. This study aimed to assess nurses’ and physicians’ attitudes towards nurse-physician collaboration in critical care areas in one teaching hospital in Saudi Arabia. A cross-sectional study design was conducted, and the data were collected from both nurses and physicians (n = 239) who were working in critical care areas in one teaching hospital in Jeddah city by using Jefferson scale of attitudes toward nurse-physician collaboration. Data were analysed by using t-test, one-way ANOVA and pearson correlation. The results demonstrated that nurses showed more positive attitudes towards collaboration in critical care areas than physicians. This study concluded that teamwork and collaboration must be encouraged among both nurses and physicians within the critical care units. Furthermore, interprofessional education for both nurses and physicians must be provided within the educational programs to increase the awareness regarding the importance of interproffesional education among healthcare providers.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.514
Teacher spread0.487 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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