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Record W3008085110 · doi:10.1371/journal.pone.0229385

Trajectories and mental health-related predictors of perceived discrimination and stigma among homeless adults with mental illness

2020· article· en· W3008085110 on OpenAlexafffundabout
Cília Mejía-Lancheros, James Lachaud, Patricia O’Campo, Kathryn Wiens, Rosane Nisenbaum, Ri Wang, Stephen W. Hwang, Vicky Stergiopoulos

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental HealthPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchMental Health CommissionOntario Ministry of Health and Long-Term Care
KeywordsStigma (botany)Mental healthMental illnessPsychiatryClinical psychologyPsychologyMultinomial logistic regressionMoodHousing FirstMedicine

Abstract

fetched live from OpenAlex

Stigma and discrimination toward individuals experiencing homelessness and mental disorders remain pervasive across societies. However, there are few longitudinal studies of stigma and discrimination among homeless adults with mental illness. This study aimed to identify the two-year group trajectories of stigma and discrimination and examine the predictive role of mental health characteristics among 414 homeless adults with mental illness participating in the extended follow-up phase of the Toronto At Home/Chez Soi (AH/CS) randomized trial site. Mental health-related perceived stigma and discrimination were measured at baseline, one, and two years using validated scales. Group-based-trajectory modelling was used to identify stigma and discrimination group trajectory memberships and the effect of the Housing First treatment (rent supplements and mental health support services) vs treatment as usual on these trajectories. The associations between mental health-related characteristics and trajectory group memberships were also assessed using multinomial logistic regression. Over two-years, three group trajectories of stigma and discrimination were identified. For discrimination, participants followed a low, moderate, or increasingly high discrimination group trajectory, while for stigma, participants followed a low, moderate or high stigma group trajectory. The Housing First treatment had no significant effect on discrimination or stigma trajectories groups. For the discrimination trajectories, major depressive episode, mood disorder with psychotic features, alcohol abuse, suicidality, severity of mental health symptoms, and substance use severity in the previous year were predictors of moderate and increasingly high discrimination trajectories. History of discrimination within healthcare setting was also positively associated with following a moderate or high discrimination trajectory. For the stigma trajectories, substance dependence, high mental health symptoms severity, substance use severity, and discrimination experiences within healthcare settings were the main predictors for the moderate trajectory group; while substance dependence, suicidality, mental health symptom severity, substance use severity and discrimination experiences within health care setting were also positive predictors for the high stigma trajectory group. Ethno-racial status modified the association between having a major depression episode, alcohol dependence, and the likelihood of being a member of the high stigma trajectory group. This study showed that adults experiencing mental illness and homelessness followed distinct stigma and discrimination group trajectories based on their mental health-problems. There is an urgent need to increase focus on strategies and policies to reduce stigma and discrimination in this population.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.294
Teacher spread0.259 · 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

Citations72
Published2020
Admission routes3
Has abstractyes

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