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Record W7105836427 · doi:10.1080/10530789.2025.2589476

A qualitative examination of the challenges and needs during hospital discharge for people with lived experience of homelessness

2025· article· en· W7105836427 on OpenAlexafffundabout

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British ColumbiaProvidence Health CareConcordia UniversitySimon Fraser University
FundersGovernment of Canada
KeywordsLived experienceHospital dischargeQualitative researchDischarge planningPatient dischargeQualitative analysis

Abstract

fetched live from OpenAlex

People with lived or living experiences of homelessness (PWLEH) have higher rates of chronic and acute health problems and present to hospitals at a higher rate than their housed counterparts. Limited research has explored how PWLEH characterize their own discharge experiences. To improve hospital discharge processes and policies for PWLEH, this qualitative study conducted a thematic analysis of semi-structured interviews with 20 PWLEH in Metro Vancouver, Canada. Participants reported challenges to hospital discharge planning, including (1) stigma, discrimination, and negative interactions with hospital staff, (2) limited communication and collaboration with hospital staff, (3) rushed and careless treatment, and (4) losing shelter/housing while in the hospital. For effective discharge planning, reported needs included (1) opportunities for self-determination, (2) increased communication and information, (3) support from formal and informal networks, (4) a person-centered approach to care, (5) transportation upon discharge, and (6) increased affordable housing. Study findings highlight factors that influence hospital discharge experiences for PWLEH at the individual, interpersonal, and systems levels and suggest opportunities for improvements in discharge policies and practices.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.383
Teacher spread0.343 · 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 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
Published2025
Admission routes3
Has abstractyes

Explore more

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