MétaCan
Menu
Back to cohort
Record W3025768417 · doi:10.1017/s0714980820000215

COVID-19: Pandemic Risk, Resilience and Possibilities for Aging Research

2020· article· en· W3025768417 on OpenAlexaffabout
Andrew Wister, Mark Speechley

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsWestern UniversitySimon Fraser University
Fundersnot available
KeywordsPandemicPsychological resilienceSocial isolationCoronavirus disease 2019 (COVID-19)Isolation (microbiology)Resilience (materials science)Economic growthDevelopment economicsPolitical scienceGerontologyPsychologyMedicineSocial psychologyDiseaseBiologyPsychiatryEconomicsInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The COVID-19 global crisis is reshaping Canadian society in unexpected and profound ways. The significantly higher morbidity and mortality risks by age suggest that this is largely a "gero-pandemic," which has thrust the field of aging onto center stage. This editorial emphasizes that vulnerable older adults are also those most affected by COVID-19 in terms of infection risk, negative health effects, and the potential deleterious outcomes on a range of social, psychological, and economic contexts - from ageism to social isolation. We also contend that the pathogenic analysis of this pandemic needs to be balanced with a salutogenic approach that examines the positive adaptation of people, systems and society, termed COVID-19 resilience. This begs the question: how and why do some older adults and communities adapt and thrive better than others? This examination will lead to the identification and response to research and data gaps, challenges, and innovative opportunities as we plan for a future in which COVID-19 has become another endemic infection in the growing list of emerging and re-emerging pathogens.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.384
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 teacher head, not a consensus.

Study designNot applicable
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

Citations67
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicResilience and Mental HealthFrench-language works237,207