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Record W4200446318 · doi:10.31219/osf.io/rs56k

Nothing about us without us: Canadian vaccine decision making must involve disabled people

2021· preprint· en· W4200446318 on OpenAlexaboutno aff
Holly O. Witteman, Gabrielle W. Peters, Cassandra Vujovich‐Dunn, Amine Ouertani, Sharmistha Mishra

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsPrioritizationNothingPandemicVaccinationCoronavirus disease 2019 (COVID-19)DiseasePolitical scienceBusinessMedicineActuarial sciencePublic relationsPsychologyEconomic growthEconomicsImmunologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Across Canada, national and provincial Covid-19 vaccine prioritization guidance and strategies have failed to appropriately include people with disabilities. Since the early goal of vaccination was to reduce severity, those at higher risk of severe disease if infected were meant to be prioritized early in vaccination campaigns, directly reducing their chance of death due to Covid-19. Older adults and some other higher-risk groups were therefore accorded high priority. However, younger disabled people were not prioritized for vaccines at levels commensurate with their risk of severe Covid-19 outcomes. Consequently, Canadian national policy recommendations have been incongruent with peer countries’ vaccine prioritization, scientific evidence, and priorities expressed by Canadians regarding how we should allocate Covid-19 vaccines. To avoid repeating these mistakes, current and future pandemic planning must include disabled people as full members of decision-making committees, in keeping with the longstanding demand of disabled people: “Nothing about us without us.” (1)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.083
Threshold uncertainty score0.602

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0270.024
Scholarly communication0.0150.006
Open science0.0020.006
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0080.001

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 head0.021
GPT teacher head0.312
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2021
Admission routes1
Has abstractyes

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