MétaCan
Menu
Back to cohort
Record W2138553767 · doi:10.3109/09638237.2014.910640

Barriers to obtaining employment for people with severe mental illness experiencing homelessness

2014· article· en· W2138553767 on OpenAlexafffund
Daniel Poremski, Rob Whitley, Éric Latimer

Bibliographic record

VenueJournal of Mental Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsDouglas Mental Health University InstituteMcGill University
FundersHealth CanadaMental Health CommissionMcGill University
KeywordsMental illnessPsychological interventionPsychiatryUnemploymentSubstance abusePsychologyMental healthQualitative researchSupported employmentMedicineClinical psychologyWork (physics)Sociology

Abstract

fetched live from OpenAlex

BACKGROUND: The rate of unemployment among homeless people is estimated to exceed 80%. A high prevalence of mental illness partially explains this figure, but few studies about the relationship between employment and homelessness have focused on homeless people with mental illness. AIM: The present study explores the self-reported barriers to employment in a sample of individuals with mental illness when they were homeless. METHODS: A sample of 27 individuals with mental illness and recent experiences of homelessness, who had expressed an interest in working, participated in semi-structured qualitative interviews. Inductive analysis was used to identify barriers to employment. FINDINGS: The prominent barriers include: (1) current substance abuse, (2) having a criminal record, (3) work-impeding shelter practices, and (4) difficulties obtaining adequate psychiatric care. CONCLUSION: Individuals who have been homeless and have a mental illness report facing specific barriers associated with mental illness, homelessness, or the interaction between the two. Additional research should explore how supported housing and employment interventions can be tailored to effectively serve this group.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.027
GPT teacher head0.393
Teacher spread0.366 · 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

Citations72
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Mental HealthSame topicHomelessness and Social IssuesFrench-language works237,207