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Record W3127776454 · doi:10.3138/cpp.2021-012

Perceptions of Canadian Federal Policy Responses to COVID-19 among People with Disabilities and Chronic Health Conditions

2021· article· en· W3127776454 on OpenAlexafffundvenueabout
David Pettinicchio, Michelle Maroto, Martin Lukk

Bibliographic record

VenueCanadian Public Policy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of AlbertaUniversity of Toronto
FundersEmployment and Social Development CanadaUniversity of TorontoUniversity of Alberta
KeywordsPolitical scienceCoronavirus disease 2019 (COVID-19)ReceiptGovernment (linguistics)Public policyHumanitiesPoliticsPolitical actionPublic administrationMedicineLawBusiness

Abstract

fetched live from OpenAlex

This study examines how people with disabilities and chronic health conditions—members of a large and diverse group often overlooked by Canadian public policy—are making sense of the Canadian federal government’s response to COVID-19. Using original national online survey data collected in June 2020 ( N = 1,027), we investigate how members of this group view the government’s overall response. Although survey results show broad support for the federal government’s pandemic response, findings also indicate fractures based on disability type and specific health condition, political partisanship, region, and experiences with COVID-19. Among these, identification with the Liberal party and receipt of CERB stand out as associated with more positive views. Further examination of qualitative responses shows that these views are also linked to differing perspectives surrounding government benefits and spending, partisan divisions, and other social and cultural cleavages.

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)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.375
Teacher spread0.332 · 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

Citations15
Published2021
Admission routes4
Has abstractyes

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