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Record W3150599964 · doi:10.1002/oby.23182

Protecting individuals living with overweight and obesity: Attitudes and concerns toward COVID‐19 vaccination in Canada

2021· article· en· W3150599964 on OpenAlexaffabout
Michael Vallis, Stephen Glazer

Bibliographic record

VenueObesity · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVaccinationMedicineOverweightObesityCoronavirus disease 2019 (COVID-19)Confidence intervalGerontologyDemographyFamily medicineDiseaseImmunologyInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to assess attitudes toward coronavirus disease 2019 (COVID-19) vaccination and the degree of fear of COVID-19 among those living with obesity. METHODS: Two samples were recruited for an online survey: one sample was a representative sample of Canadians living with overweight and obesity (n = 1,089), and the second was a convenience sample of individuals recruited from obesity clinical services or patient organizations (n = 980). Respondents completed ratings of their comfort receiving a COVID-19 vaccination along with the Vaccine Hesitancy Scale and the Fear of COVID-19 Scale. RESULTS: Approximately one-third of respondents, regardless of sample or weight category, were not comfortable receiving a vaccination, and one-half expressed moderate or greater perceived risks of vaccination. Confidence in vaccinations was extremely low, especially for those in the clinical sample. Fear of COVID-19 was substantial and predicted attitudes toward vaccination. Females were less comfortable receiving the vaccine and perceived more risks than males. CONCLUSIONS: These results suggest those living with obesity are highly ambivalent about COVID-19 vaccination. Despite their being at high risk, their confidence in vaccines is very low. Results suggest the need for patient-centered counseling, with a focus on shared decision-making to strengthen confidence and reduce perceived risk.

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)0.001
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 score0.274
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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