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Record W4311008576 · doi:10.5206/ijoh.2022.2.14968

A Scan of Ontario Cities’ COVID-19 Policies and their Impacts on People Living in Homelessness

2022· article· en· W4311008576 on OpenAlexafffundvenueabout
Kayla May, Jacob T. Shelley

Bibliographic record

VenueInternational Journal on Homelessness · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWestern University
FundersMcMaster University
KeywordsPandemicSanitationPublic healthCoronavirus disease 2019 (COVID-19)BusinessSocial distanceIsolation (microbiology)Public policyEconomic growthPolitical scienceEnvironmental healthSocioeconomicsMedicineSociologyEconomicsNursing

Abstract

fetched live from OpenAlex

This article summarizes findings from a scan of COVID-19 policies in Ontario that impact individuals experiencing homelessness. We collected and analyzed policy data between March 2020 and August 2020, from 10 cities, including all municipal-level restrictions and public health measures implemented in response to COVID-19. From our scan, we found that 161 policies had direct or indirect implications for people living in homelessness. These policies were organized into categories that describe ‘where’ effects were seen – which most often relate to requirements for physical distancing. Some of the most obvious impacts relate to reduced access to needed services and supports in light of non-essential business closures and other service disruptions. Other key impacts relate to the use of public spaces during the pandemic - including access to sanitation facilities, encampment bylaws, changes to public transit services, quarantine and isolation mandates, and the impact of a province-wide stay-at-home order. Overall, in reviewing local responses to the pandemic, it is critical to consider the disproportionate impacts of restrictive public health measures on already marginalized groups and continue to learn about strategies that aim to protect all members of society.

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)0.002
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 score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.045
GPT teacher head0.387
Teacher spread0.342 · 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

Citations4
Published2022
Admission routes4
Has abstractyes

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