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Record W3199951399 · doi:10.1002/14651858.cd015112

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings

2021· article· en· W3199951399 on OpenAlexaff
Ana Beatriz Pizarro, Emma Persad, Solange Durão, Barbara Nußbaumer-Streit, Chantelle Garritty, Jean S Engela-Volker, Damien McElvenny, Sarah Rhodes, Katie Stocking, Tony Fletcher, Martie van Tongeren, Craig A. Martin, Kukuh Noertjojo, Olivia Sampson, Karsten Juhl Jørgensen, Matteo Bruschettini

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsWorkers Compensation Board of British ColumbiaPublic Health Agency of Canada
Fundersnot available
KeywordsPsychological interventionHealth careSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineCoronavirus disease 2019 (COVID-19)Intensive care medicineNursingInternal medicinePolitical scienceDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings

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)0.022
metaresearch head score (Gemma)0.059
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: none
Teacher disagreement score0.053
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.059
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.012
Bibliometrics0.0070.007
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0530.004

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 head0.101
GPT teacher head0.409
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 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

Citations11
Published2021
Admission routes1
Has abstractyes

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