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Record W4293693007 · doi:10.1016/j.ssmqr.2022.100163

“They have their security, we have our community”: Mutual support among people experiencing homelessness in encampments in Toronto during the COVID-19 pandemic

2022· article· en· W4293693007 on OpenAlexaffabout
Lisa M. Boucher, Zoë Dodd, Samantha Young, Abeera Shahid, Ahmed M. Bayoumi, Michelle Firestone, Claire Kendall

Bibliographic record

VenueSSM - Qualitative Research in Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoRegent Park Community Health CentreSt. Michael's HospitalBruyèreUniversity of Ottawa
FundersNational Institute on Drug Abuse
KeywordsPandemicContext (archaeology)Coronavirus disease 2019 (COVID-19)Suicide preventionPoison controlOccupational safety and health2019-20 coronavirus outbreakSociologySocioeconomicsGeographyEconomic growthEnvironmental healthPolitical scienceMedicineVirology

Abstract

fetched live from OpenAlex

Unaffordable housing is a growing crisis in Canada, exacerbated by the COVID-19 pandemic, yet perspectives of people living outdoors in encampments have primarily gone unheard. We conducted qualitative interviews with encampment residents to explore how mutual support occurred within the social context of encampments. We found that mutually supportive interactions helped residents meet basic survival needs, as well as health and social needs, and reduced common health and safety risks related to homelessness. The homelessness sector should acknowledge that encampment residents form their own positive communities, and ensure policies and services do not isolate people from these beneficial social connections.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.016
Scholarly communication0.0040.002
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.384
GPT teacher head0.619
Teacher spread0.235 · 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 designQualitative
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

Citations14
Published2022
Admission routes2
Has abstractyes

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