MétaCan
Menu
Back to cohort
Record W4313487835 · doi:10.1080/07448481.2022.2155470

Vaccination rates among international students: Insights from a university health vaccination initiative

2023· article· en· W4313487835 on OpenAlexaboutno aff
ChengChing Hiya Liu, Jiying Ling, Charles Liu, Kara Schrader, Ravichandran Ammigan, Emily Mclntire

Bibliographic record

VenueJournal of American College Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationMedicineDiphtheriaFamily medicineQuarter (Canadian coin)Health careEnvironmental healthImmunologyPolitical science

Abstract

fetched live from OpenAlex

Objective: To examine the effects of a university’s health vaccination initiative in increasing vaccination rates among international students/scholars in the United States. Methods: The vaccination initiative included: increasing vaccination opportunities by holding a pre-registration event, providing vaccine recommendations from healthcare professionals including a bilingual health interpreter, implementing campus-based marketing strategies, sending reminders using social media, and offering free and affordable vaccines. Results: Total 575 international students/scholars attended from 2016 to 2019 (N = 118, 163, 193, and 101, respectively), showing an increase compared to 2015. The most common vaccines administered were for influenza, human papillomavirus (HPV), tetanus, diphtheria, and acellular pertussis (Tdap), and Hepatitis A. Slightly less than one-quarter of participants received three or more vaccines. More women than men received HPV vaccine. Participants shared they would not have been vaccinated without this initiative and wished for more vaccination events. Conclusions: Future efforts are needed to implement this initiative across universities to further evaluate its effectiveness.

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 score0.233
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.358
Teacher spread0.332 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of American College HealthSame topicVaccine Coverage and HesitancyFrench-language works237,207