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Record W3214828911 · doi:10.1038/s43587-021-00128-1

A longitudinal analysis of the impact of the COVID-19 pandemic on the mental health of middle-aged and older adults from the Canadian Longitudinal Study on Aging

2021· article· en· W3214828911 on OpenAlexafffundabout
Parminder Raina, Christina Wolfson, Lauren E. Griffith, Susan Kirkland, Jacqueline M. McMillan, Nicole E. Basta, Divya Joshi, Urun Erbas Oz, Nazmul Sohel, Geva Maimon, Mary E. Thompson, Andrew P. Costa, Laura N. Anderson, Cynthia Balion, Benoît Cossette, Mélanie Levasseur, Scott M. Hofer, Theone Paterson, David B. Hogan, Teresa Liu‐Ambrose, Verena Menec, Philip St. John, Gerald Mugford, Zhiwei Gao, Vanessa Taler, Patrick S. R. Davidson, Andrew Wister, Theodore D. Cosco

Bibliographic record

VenueNature Aging · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaQuebec Rehabilitation Research NetworkDalhousie UniversityMemorial University of NewfoundlandUniversity of WaterlooSimon Fraser UniversityUniversity of OttawaMcGill UniversityMcGill University Health CentreUniversité de SherbrookeMcMaster UniversityUniversity of VictoriaUniversity of CalgaryImpactMcMaster University Medical Centre
FundersCanadian Institutes of Health ResearchPublic Health Agency of CanadaGovernment of CanadaPublic Health AgencyCanada Foundation for InnovationMcMaster University
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Longitudinal studyMental health2019-20 coronavirus outbreakGerontologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Longitudinal dataPsychologyMedicineDemographyPsychiatryVirologySociologyDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.006
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 score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.007
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.404
Teacher spread0.329 · 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

Citations112
Published2021
Admission routes3
Has abstractno

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