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Record W3209486726 · doi:10.1101/2021.10.21.21265355

COVID-19 Vaccination Mandates and Vaccine Uptake

2021· preprint· en· W3209486726 on OpenAlexaffabout
Alexander Karaivanov, Dongwoo Kim, Shih En Lu, Hitoshi Shigeoka

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCounterfactual thinkingVaccinationMandateCoronavirus disease 2019 (COVID-19)Government (linguistics)PopulationDemographyMedicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceEnvironmental healthVirologyPsychologyInternal medicineLaw

Abstract

fetched live from OpenAlex

Abstract We evaluate the impact of government-mandated proof of vaccination requirements for access to public venues and non-essential businesses on COVID-19 vaccine uptake. We find that the announcement of a mandate is associated with a rapid and significant surge in new vaccinations (more than 60% increase in weekly first doses), using the variation in the timing of these measures across Canadian provinces in a difference-in-differences approach. Time-series analysis for each province and for France, Italy and Germany corroborates this finding. Counterfactual simulations using our estimates suggest the following cumulative gains in the vaccination rate among the eligible population (age 12 and over) as of October 31, 2021: up to 5 percentage points (p.p.) (90% CI 3.9–5.8) for Canadian provinces, adding up to 979,000 (425,000-1,266,000) first doses in total for Canada (5 to 13 weeks after the provincial mandate announcements), 8 p.p. (4.3–11) for France (16 weeks post-announcement), 12 p.p. (5–15) for Italy (14 weeks post-announcement) and 4.7 p.p. (4.1–5.1) for Germany (11 weeks post-announcement).

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.000

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 head0.036
GPT teacher head0.336
Teacher spread0.299 · 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.

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

Citations16
Published2021
Admission routes2
Has abstractyes

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