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Record W4387137238 · doi:10.3390/ijerph20196845

Bridge Healing: A Pilot Project of a New Model to Prevent Repeat “Social Admit” Visits to the Emergency Department and Help Break the Cycle of Homelessness in Canada

2023· article· en· W4387137238 on OpenAlexafffundabout
Matthew Robrigado, Igor Zorić, David A. Sleet, Louis Hugo Francescutti

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Alberta
FundersAlberta Health Services
KeywordsGrassrootsBridge (graph theory)General partnershipPublic housingEmergency departmentPilot programTransitional careHealth careBusinessMedicineNursingPolitical scienceEngineeringCivil engineeringMedical educationFinance

Abstract

fetched live from OpenAlex

Homelessness continues to be a pervasive public health problem throughout Canada. Hospital Emergency Departments (EDs) and inpatient wards have become a source of temporary care and shelter for homeless patients. Upon leaving the hospital, homeless patients are not more equipped than before to find permanent housing. The Bridge Healing program in Edmonton, Alberta, has emerged as a novel approach to addressing homelessness by providing transitional housing for those relying on repeated visits to the ED. This paper describes the three essential components to the Bridge Healing model: partnership between the ED and a Housing First community organization; facility design based on The Eden Alternative™ principles; and grassroots community funding. This paper, in conjunction with the current pilot project of the Bridge Healing facilities, serves as a proof of concept for the model and can inform transitional housing approaches in other 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 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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.182
GPT teacher head0.478
Teacher spread0.295 · 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 designObservational
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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public Health→Same topicHomelessness and Social Issues→French-language works237,207→