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Record W4280566419 · doi:10.1177/07334648221092029

COVID-19 Vaccinations and Anxiety in Middle-Aged and Older Jews and Arabs in Israel: The Moderating Roles of Ethnicity and Subjective Age

2022· article· en· W4280566419 on OpenAlexaff
Yoav S. Bergman, Yuval Palgi, Boaz M. Ben‐David, Ehud Bodner

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAnxietyEthnic groupFeelingContext (archaeology)MedicinePsychologyClinical psychologyGerontologyDemographyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Subjective age (i.e., feeling younger/older than one’s chronological age) plays a significant role in older minority group members’ psychological well-being. In light of the importance of vaccinations for fighting COVID-19, it is unclear whether ethnicity and subjective age moderate the connection between receiving COVID-19 vaccinations and anxiety in Israel. Jewish ( n = 198) and Arab older adults ( n = 84) provided information regarding COVID-19 vaccinations, subjective age, and anxiety symptoms, as well as additional socio-demographic and COVID-19-related health factors (age range= 40–100, M = 62.5, SD = 12.34). Results demonstrated that feeling older was associated with increased anxiety ( p < .001) and that vaccinations were linked to increased anxiety among Jews ( p < .05). Moreover, the association between COVID-19 vaccinations and anxiety was significant only among Jewish participants with an older subjective age ( p < .05). We stress the importance of examining cultural diversities regarding the contribution of subjective age in the context of COVID-19 and psychological well-being.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.219
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.321
Teacher spread0.278 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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