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Record W3135501193 · doi:10.47326/ocsat.2021.02.13.1.0

Early Impact of Ontario’s COVID-19 Vaccine Rollout on Long-Term Care Home Residents and Health Care Workers

2021· report· en· W3135501193 on OpenAlexaboutno aff
Kevin A. Brown, Nathan M. Stall, Thuva Vanniyasingam, Sarah A. Buchan, Nick Daneman, Michael Hillmer, Jessica Hopkins, Jennie Johnstone, Antonina Maltsev, Allison McGeer, Beate Sander, Rachel Savage, Tania H. Watts, Peter Jüni, Paula A. Rochon

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsLong-term careMedicineCoronavirus disease 2019 (COVID-19)VaccinationPopulationHealth careEnvironmental healthFamily medicineNursingVirologyInfectious disease (medical specialty)DiseaseInternal medicine

Abstract

fetched live from OpenAlex

The rollout of COVID-19 vaccines to Ontario’s long-term care (LTC) homes has substantially reduced SARS-CoV-2 infections, COVID-19 hospitalizations, and COVID-19 deaths among LTC residents and health care workers. Completing and maximizing the uptake of the full COVID-19 vaccine series according to recommended schedules will maximize the safety and well-being of Ontario’s LTC residents and staff.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.403
Teacher spread0.351 · 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

Citations29
Published2021
Admission routes1
Has abstractyes

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