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Record W4412347307 · doi:10.63332/joph.v4i3.2921

The Role of Nursing in Seasonal Crowd Management

2024· article· en· W4412347307 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Posthumanism · 2024
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsNursing managementNursingPsychologyMedicine

Abstract

fetched live from OpenAlex

Every year the Ministry of Health in Saudi Arabia announces the opening of applications to those who wish to work on Hajj. A certain number of applicants are nominated, yet many nurses continue to apply to participate during Hajj ever year. Although nurses are under great work pressure during Hajj, many nurses still apply for the approval to participate in Hajj. But as a life savior, communicator, therapist and first responder in case of mass gathering and disaster, the role of nurses is inevitable. The aim of this study is to identify the motivations of nurses to work during Hajj. Furthermore, to explore the motivations that lead nurses to participate in the Hajj season and to explore the challenges faced during the Hajj season. The study used a descriptive qualitative study design. The data were analyzed using SPSS Ver. 22.0.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.014
GPT teacher head0.323
Teacher spread0.309 · 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