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Record W1662284843 · doi:10.3233/wor-2012-1315

Transitions to work for persons with serious mental illness in northeastern Ontario, Canada: Examining barriers to employment

2012· article· en· W1662284843 on OpenAlexafffundabout
Karen Rebeiro Gruhl

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsHealth Sciences North
FundersCanadian Institutes of Health Research
KeywordsConceptualizationMental illnessMainstreamMental healthHousing FirstFocus groupWork (physics)Service providerQualitative researchPsychologyPublic relationsVocational educationSupported employmentSociologyService (business)Political scienceBusinessPsychiatryMarketingPedagogySocial science

Abstract

fetched live from OpenAlex

This paper considers the importance of place in the conceptualization of transitions to work for persons with serious mental illness (SMI). A qualitative case study was conducted to explore the influence of place on access to employment for persons with SMI. In-depth interviews, focus groups, and demographic data collected from urban and rural residing individuals who experience SMI, mental health and vocational service providers, and decision makers across northeastern Ontario inform this paper. The results highlight the primary theme, stuck in the mud, which explains how people with SMI, service providers and decision makers are stuck regarding employment. Ultimately, their being stuck creates a variety of place-related tensions and a tendency to settle for less in the area of employment for persons with SMI. The condition of being stuck in the mud is expressed as a metaphor depicting the existing tensions between ideas which govern provincial employment policy for persons with SMI and the mainstream or dominant discourse governing local organizations, programs and practices in the case communities and supports the need to consider place in policy implementation.

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 score0.434
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.316
Teacher spread0.279 · 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

Citations9
Published2012
Admission routes3
Has abstractyes

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