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Record W4392134487 · doi:10.53555/sfs.v10i5.2181

The Extent Of Cooperation Between Doctors And Nurses In Emergency Departments In Health Facilities

2023· article· en· W4392134487 on OpenAlexvenueno aff
Ayman. A. Jaha, Hamed. O. Alqurashi, Ahmad. J. Alharbi, Ghassan Ghazi Marghalani, Adel. S. Eid, Hanan. H. Alnemari, Dalal Mohammed Alharthi, Khalid. L. Allahyani, Tahani. A. Alrwemi, Ahmed Ali Alharbi, Mohammad Sayer M Alharbi, Dina. N. Alsudani, Mona. S. Alharbi, Fakher. H. Alhassani, Basim. A. Alsadi, Sitti. S. Almalayou, Mahdi. A. Alyamani, Hassan. A. Alkhattabi, Shadia. A. Althbiti, Ohoud. S. Alharthi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
FundersUmm Al-Qura University
KeywordsMedical emergencyNursingBusinessPsychologyMedicine

Abstract

fetched live from OpenAlex

This current study aims to what is the extent of cooperation between doctors and nurses in emergencies, what is the role assigned to nursing in this field, what is the role assigned to this field, the questionnaire was conducted via the Google Drive program, and after that this questionnaire was distributed to all residents of the city of Mecca through the social network (WhatsApp), where the presence of the Corona virus and people’s unwillingness to communicate directly was taken into account, as it was taken into account. 700 questionnaires were distributed (the target population is residents of the city of Mecca), and responses were obtained from the researcher’s email (from age 25-55years in Mecca). The data was collected and analyzed through the use of a table, the Excel 2010 program, a pie chart, and a photograph of the data.

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)0.009
metaresearch head score (Gemma)0.036
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.246
GPT teacher head0.468
Teacher spread0.222 · 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
Published2023
Admission routes1
Has abstractyes

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