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Record W6963871852 · doi:10.23668/psycharchives.5360

COVID-19 Snapshot Monitoring in Canada (COSMO Canada): Monitoring Citizens’ Perceptions, Knowledge, and Behaviours relating to the Pandemic (Part II)

2022· article· en· W6963871852 on OpenAlexaboutno aff

Bibliographic record

VenuePsychology Archives · 2022
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsSnapshot (computer storage)PandemicData collectionThematic analysisPublic healthContext (archaeology)Coronavirus disease 2019 (COVID-19)Public health surveillanceGovernment (linguistics)

Abstract

fetched live from OpenAlex

From April 2020 to December 2021, Privy Council Office (PCO) led the implementation of the COVID-19 Snapshot Monitoring (COSMO) Study Phase 1 which longitudinally captured respondents’ perceptions, knowledge, and behaviours in response to the pandemic. The COSMO Study Phase 1 was founded on a comprehensive survey developed by the World Health Organization (WHO), released to assist countries in quickly establishing a data collection mechanism to track the evolving response context related to COVID-19 at the citizen-level. It has enabled PCO to contribute evidence-based, behaviourally-informed insights and recommendations to public communication materials, policy and programmatic considerations, and whole-of-government decision-making related to the Government of Canada’s broader response effort. The first phase of the study collected data across sixteen waves spanning April 2020 to November 2021. This protocol details the COSMO Study’s second Phase, which will continue to monitor the evolving pandemic response context with a new sample of Canadians (including a flexible oversample of diverse sub-segments of the population) and a restructured survey body exploring new thematic areas related to reintegration and recovery.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
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 head0.220
GPT teacher head0.436
Teacher spread0.216 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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