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Record W3076197966 · doi:10.12927/hcpol.2020.26294

Hospital Discharge Planning for People Experiencing Homelessness Leaving Acute Care: A Neglected Issue

2020· article· en· W3076197966 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueHealthcare policy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMinistry of Transportation of OntarioHealth Sciences CentreSunnybrook Health Science CentreMcGill University Health CentrePublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsDischarge planningHospital dischargeAcute careMedicineHealth careAcute hospitalNursingPatient dischargeMedical emergencyMEDLINEIntensive care medicinePolitical science

Abstract

fetched live from OpenAlex

People experiencing homelessness have worse health outcomes than the general population and limited access to primary/preventative healthcare. This leads to high hospital readmission rates. Effective discharge planning can improve recovery rates and reduce hospital costs. However, most hospital discharge policies and best practice guidelines are not tailored to patients with no fixed address, contributing to inappropriate discharges and health inequities for people experiencing homelessness. We discuss the lack of discharge policies, identifiable processes or plans specifically tailored to this population as a healthcare and policy gap, and we identify key areas for better understanding and addressing this issue.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.048
GPT teacher head0.435
Teacher spread0.387 · 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