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Record W4407523956 · doi:10.57187/s.3659

Healthcare needs, expectations and experiences of people experiencing homelessness in Western Switzerland: a qualitative and quantitative descriptive study

2025· article· en· W4407523956 on OpenAlexaffabout
Luana Schaad, Evelyne Hangartner, Chantal Berna, Justin Nikles, Laurent Hyvert, T Varela, David J.T. Campbell, Olivier Hügli, Susan E. Collins, Caroline Leblanc, Christine Loignon, Patrick Bodenmann, Véronique S. Grazioli

Bibliographic record

VenueSwiss Medical Weekly · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité de SherbrookeUniversity of Calgary
FundersFondation Leenaards
KeywordsRepresentativeness heuristicMedicineQualitative researchHealth carePopulationGerontologyNursingEnvironmental healthPsychologySociologyEconomic growthSocial psychology

Abstract

fetched live from OpenAlex

AIMS: The literature from Canada, the UK and the USA reports health inequities among people experiencing homelessness; however little is known about this population's health in Switzerland. Our study is the first to comprehensively assess health needs, expectations and experiences of people experiencing homelessness in Switzerland. METHODS: We describe the health needs, expectations and experiences of people experiencing homelessness in French-speaking Switzerland, using both quantitative and qualitative methods. From May to August 2022, 123 people experiencing homelessness completed quantitative questionnaires about health needs, expectations and experiences. Recruitment took place in 10 homeless-serving institutions across four cities in the Canton of Vaud. A total of 18 people experiencing homelessness and 13 professionals involved in the homeless-serving sector completed qualitative interviews. For the qualitative strand, we selected people experiencing homelessness using quota sampling based on health insurance, residency status and sex representativeness according to the study population. For homeless-serving sector professionals, we used quota sampling by professions (i.e. night watcher in shelters; social/healthcare workers) ensuring balance. In addition, we aimed to recruit at least one homeless-serving sector professional from each of the ten institutions included in the parent research project. RESULTS: The most common health issues reported were musculoskeletal, dental and psychiatric. Thirty-one percent of people experiencing homelessness had visited emergency rooms and 27% a community health centre in the prior 6 months. People experiencing homelessness reported low quality of life according to the WHOQOL, especially in social and environmental domains; 33% reported moderate and 17% high grade of psychological distress. Findings indicated that up to 32% of participants reported facing difficulties in reaching out to the healthcare system. In qualitative interviews, people experiencing homelessness described positive perceptions about the Swiss healthcare system. However, people experiencing homelessness reported various barriers encountered while seeking healthcare (e.g., health insurance, financial barriers, appointment delays, hesitancy in accessing care, prioritising other needs). Both groups commonly reported that social situations impacted the health and healthcare use of people experiencing homelessness. CONCLUSION: People experiencing homelessness in Switzerland are not spared by the common health inequities reported in Canada, the USA and the UK. Our results provide interesting foundations on which to build public health actions towards health equity for people experiencing homelessness in Switzerland and suggest that they could benefit from additional medical follow-up and tailored interventions.

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)0.001
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 score0.112
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.064
GPT teacher head0.469
Teacher spread0.405 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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