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Record W4286588276 · doi:10.4081/gh.2022.1100

Spatial variations of COVID-19 risk by age in Toronto, Canada

2022· article· en· W4286588276 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeospatial health · 2022
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNeighbourhood (mathematics)GeographyCoronavirus disease 2019 (COVID-19)DemographyContext (archaeology)Socioeconomic statusPopulationCluster (spacecraft)GerontologyDiseaseMedicineEnvironmental healthInfectious disease (medical specialty)Sociology

Abstract

fetched live from OpenAlex

The risk of coronavirus disease 2019 (COVID-19) may vary by age, biological, socioeconomic, behavioural and logistical reasons may be attributed to these variations. In Toronto, Canada, the aging population has been severely impacted, accounting for 92% of all COVID-19 deaths. Four age groups: 60-69 years, 70-79 years, 80-89 years and ≥90 years in Toronto neighbourhoods were investigated for clustering tendencies using space-time statistics. Cohen's Kappa coefficient was computed to assess variations in risk by neighbourhood between different age groups. The findings suggest that knowledge of health risks and health behaviour varied by age across neighbourhoods in Toronto. Therefore, understanding the socioecological context of the communities and targeting age-appropriate intervention strategies is important for planning an effective mechanism for controlling the disease.

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.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.408
Teacher spread0.308 · 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