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Record W3214124124 · doi:10.5206/ijoh.2021.1.13642

Exploring the use of Hotels as Alternative Housing by Domestic Violence Shelters During COVID-19

2021· article· en· W3214124124 on OpenAlexafffundvenueabout
Tara Mantler, Jill Veenendaal, C. Nadine Wathen

Bibliographic record

VenueInternational Journal on Homelessness · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDomestic violenceContext (archaeology)AutonomyBusinessPandemicGovernment (linguistics)Focus groupEconomic growthCoronavirus disease 2019 (COVID-19)SocioeconomicsPolitical sciencePoison controlSuicide preventionEnvironmental healthGeographyMarketingSociologyMedicineEconomics

Abstract

fetched live from OpenAlex

Domestic violence shelters present a unique context within the congregate living sector in the context of the COVID-19 pandemic. Shelters supporting women during the pandemic had to change service delivery models to include housing women in hotels to adhere to government restrictions and ensure women, and their children, were not homeless. The purpose of this study was to explore the impact of hotel use as alternative housing for women experiencing domestic violence during the COVID-19 pandemic in Ontario, Canada. We used interpretive description methodology, including in-depth interviews with 8 women using shelter services, 26 shelter workers and 5 focus groups with 24 executive directors of women’s shelters and other organizations who serve women who have experienced domestic violence. We identified and explored three main tensions in housing women at hotels compared to shelters: 1) autonomy/independence versus support, 2) a better option, the only option, and/or a safety concern, and 3) adequacy of hotels as housing. Drawbacks and benefits of the use of hotels as housing for women in the context of domestic violence are explored and recommendations are highlighted.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.131
GPT teacher head0.380
Teacher spread0.249 · 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.

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

Citations21
Published2021
Admission routes4
Has abstractyes

Explore more

Same venueInternational Journal on HomelessnessSame topicIntimate Partner and Family ViolenceFrench-language works237,207