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Record W4205659320 · doi:10.5206/ijoh.2022.1.13830

COVID-19 and the Homelessness Support Sector

2022· article· en· W4205659320 on OpenAlexaffvenue
Jordan Babando, Kyler Woodmass, John R. Graham

Bibliographic record

VenueInternational Journal on Homelessness · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsSnowball samplingThematic analysisExploratory researchSocial distancePublic relationsQualitative researchService providerCoronavirus disease 2019 (COVID-19)Political scienceSociologyService (business)Economic growthBusinessMedicineMarketingSocial science

Abstract

fetched live from OpenAlex

This exploratory study sought to uncover service provider perspectives on the early response to COVID-19 in a small community in an advanced industrialized country - the homelessness support sector of the Central Okanagan, British Columbia. Following a case study approach, snowball sampling was utilized in May and June 2020 to achieve a sample size of 30 through a mix of one-on-one interviews and open-ended surveys. Qualitative thematic analysis was used to uncover commonalities among interview responses. Common themes are discussed in relation to three areas of questioning including challenges, successes, and mitigations/areas for future support. While the community came together to support the response, there were challenges and concerns regarding safety and personal protective equipment supplies, social distancing and knowledge transmission within the homeless community, access to food and water, and lack of space for isolating positive cases. The findings illustrate possible research, practice, public health policy, and emergency planning considerations within smaller communities.

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)0.003
metaresearch head score (Gemma)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.054
GPT teacher head0.412
Teacher spread0.357 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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