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Record W4411652941 · doi:10.1093/jtm/taaf058

Game on: public health readiness for upcoming FIFA world cups

2025· article· en· W4411652941 on OpenAlexaff
Habida Elachola, Shahul H. Ebrahim, Barrak Alahmad, Patricia Schlagenhauf, Ernesto Gozzer, Victoria Pando‐Robles, Ait Hadj Sliman Issam

Bibliographic record

VenueJournal of Travel Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsMedicinePreparednessPublic healthGlobal healthPublic relationsInternational Health RegulationsEnvironmental healthCoronavirus disease 2019 (COVID-19)NursingDiseasePolitical scienceInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The 2026, 2030 and 2034 FIFA World Cups present unprecedented public health challenges due to multi-country hosting, climate threats and coexisting mass gatherings. Strategic, coordinated and anticipatory health preparedness—grounded in surveillance, vaccination and risk communication—will be essential to safeguard global health, ensure participant safety and promote sustainable mass gathering management.

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)0.002
metaresearch head score (Gemma)0.008
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.200
Threshold uncertainty score0.667

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.2000.039

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 head0.120
GPT teacher head0.427
Teacher spread0.307 · 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
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

Citations4
Published2025
Admission routes1
Has abstractyes

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