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Record W2026982866 · doi:10.1177/1049732315577605

The Experiences and Perceptions of Street-Involved Youth Regarding Emergency Department Services

2015· article· en· W2026982866 on OpenAlexafffundabout
David Nicholas, Amanda S. Newton, Avery Calhoun, Kathryn Dong, Margaret A. deJong-Berg, Faye Hamilton, Christopher Kilmer, Anne-Marie McLaughlin, Janki Shankar

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMacEwan UniversityUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsEmergency departmentFocus groupGrounded theoryHealth careNursingPopulationPerceptionService (business)PsychologyMedicineQualitative researchGerontologySociologyPolitical scienceEnvironmental healthBusiness

Abstract

fetched live from OpenAlex

Street-involved (SI) youth comprise a substantial component of the urban homeless population. Despite being significant users of hospital emergency department (ED) services for acute and ongoing health needs, little is known about their experiences of ED care and the factors affecting their ED use. This study used a grounded theory and community-based approach to examine these issues. Focus groups and individual interviews were facilitated with 48 SI youth between ages 15 and 26 years, recruited in hospital or through community agencies serving SI youth in a major Western Canadian city. Results demonstrate that SI youth often perceived suboptimal care and experienced long waiting periods that led to many avoiding or prematurely exiting the ED. Service gaps appeared to have a negative bearing on their care and health outcomes. Findings invite a critical review of ED care processes, structures, and staff interactions in the aim of enhancing ED services to SI youth.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.057
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.509
GPT teacher head0.632
Teacher spread0.123 · 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.

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

Citations30
Published2015
Admission routes3
Has abstractyes

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