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Record W4224291268 · doi:10.1177/07334648221081982

Vaccine and Psychological Booster: Factors Associated With Older Adults’ Compliance to the Booster COVID-19 Vaccine in Israel

2022· article· en· W4224291268 on OpenAlexaff
Boaz M. Ben‐David, Shoshi Keisari, Yuval Palgi

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
KeywordsBooster (rocketry)VaccinationMedicineBooster doseCoronavirus disease 2019 (COVID-19)DemographicsLogistic regressionDemographyImmunizationInternal medicineImmunologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Israel became the first country to offer the booster COVID-19 vaccination. The study tested for the first time the role of sense of control (SOC) due to vaccinations, trust and vaccination hesitancy (VH), and their association with compliance to the booster COVID-19 vaccine among older adults, during the first 2 weeks of the campaign. 400 Israeli citizens (≥ 6 years old), eligible for the booster vaccine, responded online. They completed demographics, self-reports, and booster vaccination status (already vaccinated, booked-a-slot, vaccination intent, and vaccination opposers). Multinomial logistic regression was conducted with pseudo R 2 = .498. Higher SOC and lower VH were related to the difference between early and delayed vaccination (booked-a-slot, OR = 0.7 [0.49‐0.99]; 2.2 [1.32‐3.62], intent OR = 0.6 [0.42‐0.98]; 2.7 [1.52‐4.86]), as well as to rejection ( OR = 0.3 [0.11‐0.89]; 8.5 [3.39‐21.16]). Increased trust was only related to the difference between early vaccinations and vaccine rejection ( OR = 0.3 [0.11‐0.89]). We suggest that SOC, as well as low VH, can be used as positive motivators, encouraging earlier vaccinations in older age.

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 score0.106
Threshold uncertainty score0.864

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.351
Teacher spread0.269 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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