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Record W42164302 · doi:10.3233/wor-2012-1449

Employment services for persons with serious mental illness in northeastern Ontario: The case for partnerships

2012· article· en· W42164302 on OpenAlexafffundabout
Karen Rebeiro Gruhl, Carol Kauppi, Phyllis Montgomery, Susan James

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsLaurentian UniversityHealth Sciences North
FundersCanadian Institutes of Health ResearchHealth CanadaMental Health Commission
KeywordsMental illnessParticipatory action researchSupported employmentCitizen journalismGeneral partnershipRural areaMental healthBusinessEconomic growthPolitical sciencePsychologyWork (physics)PsychiatryFinance

Abstract

fetched live from OpenAlex

OBJECTIVE: To better understand why employment success is low, a case study was conducted to examine the influence of place on access to employment for persons with serious mental illness (SMI) residing in two northeastern Ontario communities (Rebeiro, in progress). METHODS: Community-based participatory research methods were used to engage persons who experience SMI, decision-makers and providers in the research. Forty-six interviews were conducted, complemented by primary and secondary quantitative data sources. RESULTS: While most consumers consider employment to be a key element of their recovery, employment rates for persons with SMI remain limited in northeastern Ontario, Canada. The findings of this case study reveal the importance of collaborative partnerships to fostering better employment outcomes in northeastern Ontario. CONCLUSION: The challenges of collaboration due to rural and northern tensions, as well as various jurisdictional and funding tensions existing at the level of community support the case for partnerships in the provision of employment services in northern and rural places.

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 score0.498
Threshold uncertainty score0.785

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 studies0.0000.000
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 head0.045
GPT teacher head0.304
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

Citations4
Published2012
Admission routes3
Has abstractyes

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