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Record W4396775217 · doi:10.1101/2024.05.07.24306964

Nowhere to Go: A parallel convergent mixed methods study examining the health of people who experience emergency shelter service restrictions

2024· preprint· en· W4396775217 on OpenAlexaffabout
Suraj Bansal, Stephanie Di Pelino, Jammy Pierre, Kathryn Chan, Amanda Lee, Olivia Mancini, Avital Pitkas, Fiona G. Kouyoumdjian, Larkin Lamarche, Robin Lennox, Marcie McIlveen, Tim O’Shea, Claire Bodkin

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsHamilton Health SciencesYork UniversityMcMaster UniversityResponse Biomedical (Canada)
Fundersnot available
KeywordsService (business)AdvertisingMedical emergencyPsychologyInternet privacyBusinessGerontologyComputer securityMarketingComputer scienceMedicine

Abstract

fetched live from OpenAlex

Abstract Background Emergency shelters offer temporary sleeping accommodation to people deprived of housing and connect them to services. Service restriction is the practice of limiting or denying someone access to emergency shelters. This parallel convergent mixed methods study describes the characteristics, healthcare utilization, and morbidity of people experiencing service restrictions in Hamilton, Ontario, and explores the relationship between health and service restriction. Methods We recruited 20 people who had experienced service restriction and accessed healthcare from the Shelter Health Network clinic. We conducted semi-structured interviews and performed reflexive thematic analysis. We reviewed participants’ medical records from January 1, 2018 to December 31, 2021 to calculate simple descriptive statistics. Mixing our qualitative and quantitative results, we generated narrative metainferences. We employed community-based research principles, including a research team with lived and living experiences of being service restricted, implementing service restrictions, or providing care to people experiencing service restrictions. Results We generated six themes: 1) Losing your home shouldn’t mean losing your humanity, 2) Where am I supposed to go?, 3) The snakes and ladders of service restrictions, 4) Abandoned to survive, 5) Constantly criminalized, 6) Harnessing the wisdom of community. Participants averaged 17.4 primary care visits, 11 emergency department visits, and 4 hospital admissions over 4 years. The most common reasons for visit were infections, traumatic injuries, and substance use-related concerns. Narrative metainferences highlighted how people experience dehumanization when accessing shelters or healthcare; how service restrictions and encampment living contribute to infections; the lack of practical supports for people using substances in shelters; the ubiquitous criminalization of people experiencing homelessness; and the care people practice for one another to reduce substance-related harms. Conclusions Participants’ high healthcare need and utilization was shaped by criminalization, stigma, societal abandonment, and abstinence-based substance use policies. Participants practiced care for themselves and others to navigate these barriers. Shelters should have a transparent service restriction process and employ harm reduction practices. Healthcare should provide affirming and accessible treatment for common conditions. Social and health services must contend with broader social forces while building on the strengths of people with lived experience to improve the health of people who are service restricted.

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)0.043
metaresearch head score (Gemma)0.051
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 score0.043
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0050.003
Scholarly communication0.0040.005
Open science0.0020.004
Research integrity0.0010.002
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 head0.141
GPT teacher head0.501
Teacher spread0.359 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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