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Record W6943903416 · doi:10.17605/osf.io/x3u9m

Effectiveness of Isolation/Quarantine in Reducing Transmission of Respiratory Infectious Diseases (i.e., COVID-19, H1N1, SARS, and MERS) and Its Impact on Individual and Societal Outcomes in Non-Healthcare Community-Based Settings: A Rapid Review of Evidence

2024· other· en· W6943903416 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQuarantineIsolation (microbiology)Transmission (telecommunications)OutbreakCoronavirus disease 2019 (COVID-19)PandemicInfectious disease (medical specialty)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Abstract

fetched live from OpenAlex

The objective of this rapid evidence synthesis is to evaluate the effectiveness of quarantine and isolation for reducing transmission of COVID-19 and other respiratory infectious diseases in non-healthcare community-based settings. Additionally, it aims to identify potential negative outcomes associated with quarantine and isolation. The rapid evidence synthesis seeks to provide the Canadian government with timely and reliable information to inform decision-making and policy development during the pandemic. Isolation and quarantine have been used to diminish the transmission of communicable diseases in cases of outbreaks all throughout history. In addition, these methods often require individuals to be away from their loved ones or confined to a specific room for an extended period of time. The aims of the present rapid systematic review is to determine the effectiveness of isolation and quarantine in decreasing the transmission of respiratory infectious diseases (i.e., COVID-19, H1N1, SARS, and MERS) as well as to determine what might be the unintended consequences of these measures.

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 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)0.015
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.086
GPT teacher head0.452
Teacher spread0.366 · 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 teacher head, not a consensus.

Study designSystematic review
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
Published2024
Admission routes1
Has abstractyes

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