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Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Post-publication record
- Nature
- Retraction
- Reason
- Breach of Policy by Author;Lack of Approval from Third Party;Removed;
- Date
- 7/22/2021 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
The record
- Venue
- PLoS ONE
- Topic
- COVID-19 diagnosis using AI
- Field
- Medicine
- Canadian institutions
- University of Ottawa
- Funders
- —
- Keywords
- Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusTracking (education)VirologyCoronavirus InfectionsMEDLINEMedicineComputer scienceBiologyPsychologyInfectious disease (medical specialty)Internal medicine
- Has abstract in OpenAlex
- no