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Record W3186878094 · doi:10.1017/ash.2021.174

Impact of mandatory online learning module for healthcare workers intending to decline influenza immunization: Implications for coronavirus disease 2019 (COVID-19)

2021· article· en· W3186878094 on OpenAlexaff
Leanne M. Delaney, Victoria R. Williams, Nick Tomiczek, Lawrence R. Robinson, Alex Kiss, Jerome A. Leis

Bibliographic record

VenueAntimicrobial Stewardship & Healthcare Epidemiology · 2021
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsImmunizationVaccinationCoronavirus disease 2019 (COVID-19)Health careMedicineInfluenza vaccineCoronavirusDiseaseVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ImmunologyInfectious disease (medical specialty)Immune systemPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

A policy mandating the completion of an online learning module for healthcare workers intending to decline influenza immunization was associated with a nearly 25% relative increase in immunization and significant reduction in healthcare-associated influenza. In the absence of mandatory vaccination, this model may help to augment severe acute respiratory coronavirus virus 2 (SARS-CoV-2) vaccine efforts.

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)0.003
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.325
GPT teacher head0.548
Teacher spread0.223 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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