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Record W4386388090 · doi:10.29333/ejeph/13627

Variables associated with deterioration in quality of life among individuals living in permanent supportive housing in Quebec during the COVID-19 pandemic

2023· article· en· W4386388090 on OpenAlexafffundabout
Lia Gentil, Marie‐Josée Fleury

Bibliographic record

VenueEuropean Journal of Environment and Public Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPandemicQuality of life (healthcare)MedicineBiopsychosocial modelGerontologyCoronavirus disease 2019 (COVID-19)Logistic regressionPublic healthMultivariate analysisEnvironmental healthPsychiatryNursingDisease

Abstract

fetched live from OpenAlex

This study aimed to identify sociodemographic and clinical characteristics and service use associated with deterioration in quality of life (QoL) among individuals residing in permanent supportive housing (PSH) during the COVID-19 pandemic. Between 2020-2022, PSH residents (n=231) were recruited from congregate and scattered site PSH in Montreal (Quebec/Canada). Multivariate logistic regression was used to test associations between QoL and PSH characteristics. Most participants (62%) reported deterioration in QoL. Decreased in PSH follow-up care and biopsychosocial services other than those from physicians, especially in basic needs, having still mental disorders (MD) or COVID-19 were found the most associated with QoL deterioration. Residing in PSH for 10+ years versus <2 years, high satisfaction with PSH, and access to public specialized outpatient services were identified as protective factors against deterioration in QoL. The findings demonstrated that comprehensive services for PSH residents may be intensified during a pandemic to protect against deterioration in QoL.

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)0.017
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.126
GPT teacher head0.378
Teacher spread0.252 · 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 designObservational
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
Published2023
Admission routes3
Has abstractyes

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