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Record W4392827595 · doi:10.53555/sfs.v10i6.2267

The Ministry Of Health Efforts To Combat Epidemics And Promote Public Health For Citizens And Visitors

2023· article· en· W4392827595 on OpenAlexvenueno aff
Majdi Noor Alasadi, Abdulghani Noor Alasadi, Jamal Saad Asiri, Majed Ali Algalibi, Ziyad Alhumidi Almutairi, Sarah Diyb Alhazmi, Ayidh Ahmed Khamis Alzahrani

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministryPublic healthEnvironmental healthPolitical scienceEconomic growthMedicineNursing

Abstract

fetched live from OpenAlex

The Ministry of Health plays a crucial role in combating epidemics and promoting public health for both citizens and visitors in any country. This essay examines the efforts of the Ministry of Health to address epidemics and enhance public health through various strategies and programs. By reviewing the methods, results, and limitations of these efforts, as well as discussing potential future directions, this essay provides a comprehensive overview of the Ministry of Health's initiatives.

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)0.027
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0040.004
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0030.004
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 head0.467
GPT teacher head0.493
Teacher spread0.026 · 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 designNot applicable
Domainnot available
GenreOther

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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