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Record W4395482339 · doi:10.1186/s12913-023-10140-3

“Access to healthcare is a human right”: a constructivist study exploring the impact and potential of a hospital-community partnered COVID-19 community response team for Toronto homeless services and congregate living settings

2024· article· en· W4395482339 on OpenAlexafffundabout
Vivetha Thambinathan, Suvendrini Lena, Jordan Ramnarine, Helen Chuang, Luwam Ogbaselassie, Marc Dagher, Elaine Goulbourne, Sheila Wijayasinghe, Jessica Bawden, Logan Kennedy, Vanessa Wright

Bibliographic record

VenueBMC Health Services Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCanada Research ChairsWomen's College Hospital
FundersWomen's College Hospital
KeywordsMedicineHealth careNursingGeneral partnershipGrounded theoryNonprobability samplingHealth administrationPopulationPublic healthQualitative researchSociologyPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals experiencing homelessness face unique physical and mental health challenges, increased morbidity, and premature mortality. COVID -19 creates a significant heightened risk for those living in congregate sheltering spaces. In March 2020, the COVID-19 Community Response Team formed at Women's College Hospital, to support Toronto shelters and congregate living sites to manage and prevent outbreaks of SARS-CoV-2 using a collaborative model of onsite mobile testing and infection prevention. From this, the Women's College COVID-19 vaccine program emerged, where 14 shelters were identified to co-design and support the administration of vaccine clinics within each shelter. This research seeks to evaluate the impact of this partnership model and its future potential in community-centered integrated care through three areas of inquiry: (1) vaccine program evaluation and lessons learned; (2) perceptions on hospital/community partnership; (3) opportunities to advance hospital-community partnerships. METHODS: Constructivist grounded theory was used to explore perceptions and experiences of this partnership from the voices of shelter administrators. Semi-structured interviews were conducted with administrators from 10 shelters using maximum variation purposive sampling. A constructivist-interpretive paradigm was used to determine coding and formation of themes: initial, focused, and theoretical. RESULTS: Data analysis revealed five main categories, 16 subcategories, and one core category. The core category "access to healthcare is a human right; understand our communities" emphasizes access to healthcare is a consistent barrier for the homeless population. The main categories revealed during a time of confusion, the hospital was seen as credible and trustworthy. However, the primary focus of many shelters lies in housing, and attention is often not placed on health resourcing, solidifying partnerships, accountability, and governance structures therein. Health advocacy, information sharing tables, formalized partnerships and educating health professionals were identified by shelter administrators as avenues to advance intersectoral relationship building. CONCLUSION: Hospital-community programs can alleviate some of the ongoing health concerns faced by shelters - during a time of COVID-19 or not. In preparation for future pandemics, access to care and cohesion within the health system requires the continuous engagement in relationship-building between hospitals and communities to support co-creation of innovative models of care, to promote health for all.

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)0.039
metaresearch head score (Gemma)0.035
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 score0.961
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0200.024
Scholarly communication0.0100.006
Open science0.0040.012
Research integrity0.0020.007
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 head0.185
GPT teacher head0.557
Teacher spread0.371 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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