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Record W2964323075 · doi:10.1177/1049732319862532

Fixed Nodes of Transience: Narratives of Homelessness and Emergency Department Use

2019· article· en· W2964323075 on OpenAlexafffund
Ross McCallum, Maria I. Medved, Diane Hiebert‐Murphy, Jino Distasio, Jitender Sareen, Dan Chateau

Bibliographic record

VenueQualitative Health Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNarrativeEmergency departmentAgency (philosophy)Context (archaeology)Narrative inquiryHealth careSociologyPopulationPsychologyQualitative researchMedicineNursingGender studiesPolitical scienceHistorySocial science

Abstract

fetched live from OpenAlex

Discourse in popular media, public policy, and academic literature contends that people who are homeless frequently make inappropriate use of hospital emergency department (ED) services. Although researchers have investigated the ED experiences of people who are homeless, no previous studies have examined how this population understands the role of the ED in their health care and in their day-to-day lives. In the present study, 16 individuals participated in semistructured interviews regarding their ED experiences, and narrative analysis was applied to their responses. Within the context of narratives of disempowerment and discrimination, participants viewed the ED in differing ways, but they generally interpreted it as a public, accessible space where they could exert agency. ED narratives were also paradoxical, depicting it as a fixed place for transient care, or a place where they were isolated yet felt a sense of belonging. Implications for policy and practice are discussed.

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)0.009
metaresearch head score (Gemma)0.019
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 score0.011
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.027
Scholarly communication0.0060.009
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.442
GPT teacher head0.629
Teacher spread0.188 · 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

Citations20
Published2019
Admission routes2
Has abstractyes

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