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Record W7017572340

Body-Map Storytelling: A Qualitative Exploration of the Lived Experiences of Hospital Care Amongst Street Involved Youth

2024· article· en· W7017572340 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsReflexivityThematic analysisParticipant observationLived experienceQualitative researchHealth careSituatedPerception
DOInot available

Abstract

fetched live from OpenAlex

This social work study explores the experiences of hospital care amongst street-involved (SI) youth. Given that SI youth are at a higher risk for health-related harms and experience significant barriers to health care services, they are at an increased likelihood of hospitalization. Yet, inadequate health care support for SI youth remains a critical problem in Canada. While hospital care amongst adult homeless populations has been well studied interdisciplinary, there is a scarcity of research investigating the hospital care experiences amongst SI youth. The aim of this qualitative, arts-based study was to explore how street-life contributes to hospitalization, how SI youth experience and perceive hospital care, and what solutions SI youth had for higher quality health care.\nGuided by anti-oppressive practice and health equity theoretical frameworks, this study situated participants as co-producers of knowledge through the process of body-map storytelling (BMST), an arts-based method of representing experience through life-sized self-portraits. Six youth, who identified as clients of a non-profit organization in Guelph Ontario, participated in the study. Participants were invited to share their stories both visually and verbally through in-depth, one-on-one art-making sessions and interviews with the researcher. Reflexive thematic analysis was used to interpret patterned meanings produced in both visual and verbal data. Ten themes were identified, fitting within four thematic categories related to the research questions: (1) participant experiences of how street life contributes to hospitalization; (2) participant experiences and perceptions of hospital care; (3) participant solutions for higher quality care based on their lived experiences; and (4) participant experiences of BMST.\nThe findings shed light on how SI youth are at a high risk for serious health consequences requiring hospital care. Findings indicate that hospital systems in Ontario continue to perpetuate oppression and inequity through inadequate delivery of care. Study participants offered solutions calling for equitable, effective, and timely care delivered by hospital staff who are skilled, knowledgeable, and compassionate. These solutions map well onto Health Quality Ontario’s framework for quality health care in Ontario. Findings also suggest that application of EQUIP health care’s Equity Oriented Health Care in hospital settings for SI youth would serve as an appropriate model of care, enabling service providers to deliver care that is trauma and violence-informed, culturally safe care/anti-racist, and harm reduction/substance use health focused.\nThis study generates knowledge about how visual research methodologies can help empower participants by engaging them in the research process. This study encourages researchers to share their lived experience as a means of destigmatizing such experiences and minimizing power disparities between researchers and participants. This study also fills a gap in the literature by generating knowledge about what high quality and equity informed health care in hospital settings might look like for 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 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)0.007
metaresearch head score (Gemma)0.008
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 score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.014
Scholarly communication0.0050.004
Open science0.0020.008
Research integrity0.0020.002
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 head0.066
GPT teacher head0.346
Teacher spread0.281 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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