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Record W3210428314 · doi:10.51224/srxiv.4

rapid review of recommendations for mitigating COVID-19 transmission in community sport and recreation facilities

2021· preprint· en· W3210428314 on OpenAlexaff
Kevin Wilson, Zachary Evans, Joseph D. Miller, Denver M. Y. Brown

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of WindsorUniversity of Waterloo
Fundersnot available
KeywordsRecreationCoronavirus disease 2019 (COVID-19)Transmission (telecommunications)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakEnvironmental planningBusinessPolitical scienceGeographyEngineeringTelecommunicationsVirologyMedicineLaw

Abstract

fetched live from OpenAlex

This rapid review was conducted to develop recommendations that can mitigate COVID-19 transmission in community sport and recreation facilities so that the industry that supports physical activity and mental health can return to a degree of normalcy. Three databases (SPORTDiscus, Web of Science, Scopus) and the WHO COVID-19 database were systematically searched for peer-reviewed literature that provided practical implications for the return to community sport and recreation facilities. The search and screening processes yielded 63 articles for full text review. The analysis resulted in 25 recommendations that were categorized in accordance with the National Institute for Occupational Safety and Health's hierarchy of controls framework for addressing occupational hazards: elimination/substitution, engineering controls, administrative controls and personal protective equipment. The results provide recommendations for public health (i.e. mandatory vaccination), architects/engineers (i.e. ventilation) and facility managers (i.e. cleaning) that can be enacted progressively in the event of future public health crises.

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)0.031
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.125
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0200.011
Science and technology studies0.0010.001
Scholarly communication0.0050.006
Open science0.0040.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0080.003

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 head0.225
GPT teacher head0.458
Teacher spread0.233 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2021
Admission routes1
Has abstractyes

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