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Record W4367353447 · doi:10.1177/17449871231159595

The perspectives of homeless people using the services of a mobile health clinic in relation to their health needs: a qualitative study on community-based outreach nursing

2023· article· en· W4367353447 on OpenAlexafffund
Étienne Paradis-Gagné, Marie‐Claude Jacques, Pierre Pariseau‐Legault, Houssem Eddine Ben-Ahmed, Ioana Ruxandra Stroe

Bibliographic record

VenueJournal of research in nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of OttawaUniversité du Québec en OutaouaisUniversité de SherbrookeUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOutreachNursingHealth careQualitative researchIntervention (counseling)MedicinePsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Background: Significant social and health issues are associated with homelessness. Negative experiences with the healthcare system are also frequent and cause people experiencing homelessness to avoid health services. Aims: The purpose of this study was to (1) explore participants' health needs concerning outreach nursing services and (2) describe the perceptions and preferences of people who access this form of community-based intervention. Methods: We conducted a critical ethnography with semi-structured interviews of 12 people experiencing homelessness who receive the services of a nurse-led mobile clinic, and 60 hours of observation during the provision of these services. Results: Our results describe the perspectives of people experiencing homelessness in three main categories: (1) worrisome health and social needs, (2) non-use of healthcare and (3) what connects us to health services. Conclusions: Timely access to healthcare is an important issue for people experiencing homelessness. Nurse-led clinics meet needs that go far beyond health issues.

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)0.046
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.326
GPT teacher head0.648
Teacher spread0.322 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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