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Record W3089222356 · doi:10.1111/hex.13106

Perceptions, needs and preferences of chronic disease self‐management support among men experiencing homelessness in Montreal

2020· article· en· W3089222356 on OpenAlexaffabout
Laura Merdsoy, Sylvie Lambert, Jessica Sherman

Bibliographic record

VenueHealth Expectations · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSt Mary's Hospital CentreSt. Mary's UniversityMcGill University
Fundersnot available
KeywordsPerceptionPeer supportSocial supportQualitative researchPsychologyMental healthDiseaseChronic diseaseEmotional supportMedicineClinical psychologyGerontologyPsychiatrySocial psychologyFamily medicineSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study explored the perceptions, needs and preferences for chronic disease self- management (SM) and SM support among men experiencing homelessness. DESIGN: A qualitative interpretive approach was used. Eighteen semi-structured interviews were conducted with 18 homeless men with a chronic disease at an emergency overnight shelter of Welcome Hall Mission (WHM) in Montreal, Quebec. Interviews were audio-recorded, transcribed verbatim and thematically analysed. RESULTS: The majority of participants perceived SM as important, described confidence to perform medical SM behaviours, and creatively adapted their SM behaviours to homelessness. Emotional SM was described as most challenging, as it was intertwined with the experience of homelessness. Three vulnerable groups were identified: (a) those with no social networks, (b) severe physical symptoms and/or (c) co-morbid mental illness. The preferred mode of delivery for SM support was through consistent contacts with health-care providers (HCPs) and peer-support initiatives. DISCUSSION AND CONCLUSIONS: Despite competing demands to fulfill basic needs, participants valued chronic disease SM and SM support. However, SM support must address complex challenges relating to homelessness including emotional SM, multiple vulnerabilities and barriers to forming relationships with HCPs.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.190
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.038
GPT teacher head0.380
Teacher spread0.341 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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