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Record W7129194282 · doi:10.7870/cjcmh-2025-016

Health Anxiety in Medically Vulnerable Canadians During Covid-19

2025· article· en· W7129194282 on OpenAlexaffvenueabout
Jori Ganetsky, Jordana L. Sommer, K. Reynolds, Natalie Mota, Charles N. Bernstein, R. El-Gabalawy

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsAnxietyDescriptive statisticsCoronavirus disease 2019 (COVID-19)Mental health2019-20 coronavirus outbreakPandemic

Abstract

fetched live from OpenAlex

Covid-19 has increased health anxiety (HA), especially among medically vulnerable groups. We examined HA trends and related factors across these groups in Canada. Using COVID Survey Canada data (PI: El-Gabalawy; N = 1,260), we assessed HA from May 2020–July 2021 using the Short Health Anxiety Inventory. Linear mixed-effects models and descriptive statistics evaluated changes and correlates. Medically vulnerable individuals showed higher HA, except older adults, who showed lower levels. HA declined non-significantly over the year in all groups. Among the medically vulnerable, clinically elevated HA was linked to greater findings that highlight elevated HA and inform prevention strategies.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.028
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.056
GPT teacher head0.425
Teacher spread0.369 · 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
Published2025
Admission routes3
Has abstractyes

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