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Record W2966637960 · doi:10.1080/10530789.2019.1648730

Strengths, challenges, and gaps in linkage to primary care among hospitalized individuals who are homeless in Vancouver, British Columbia

2019· article· en· W2966637960 on OpenAlexafffundabout
Alec Yu, Anne Gadermann, Anita Palepu

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
FundersUniversity of British ColumbiaFaculty of Medicine, University of British Columbia
KeywordsOutreachPhoneFamily medicineMedicineLinkage (software)Primary careNursingService (business)Service providerEmergency departmentMedical emergencyBusinessPolitical science

Abstract

fetched live from OpenAlex

Individuals who are homeless in the Canadian city of Vancouver, British Columbia have an increasing burden of chronic medical conditions, resulting in rising use of hospital services. Although continued efforts have been made to improve accessibility, barriers to primary care linkage and utilization still exist. Our study explored the strengths, barriers, and gaps in how inpatients who are homeless are connected to primary care through semi-structured qualitative interviews with patients (n = 22) and healthcare providers (n = 7) with experience in caring for patients who are homeless. Patients identified cell phone ownership and use of commitment devices as strengths, while clinics with long wait times, or those proximal to areas of prevalent drug use, were seen as barriers. Providers perceived that low priority was given to primary care linkage within hospitals, and that early and bidirectional communication with hospital staff is vital, particularly around discharge planning. Patients and providers then identified major gaps in service, which included a need for peer health navigation, technology-assisted outreach, and improved service distribution.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.293
Teacher spread0.283 · 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 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

Citations3
Published2019
Admission routes3
Has abstractyes

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