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Record W2151912613 · doi:10.1179/otb.2010.62.1.007

The employment rights of people with serious mental illness in Ontario: considering the influence of dominant ideology on marginalizing practices

2010· article· en· W2151912613 on OpenAlexaffabout
Karen Rebeiro Gruhl

Bibliographic record

VenueWorld Federation of Occupational Therapists Bulletin · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsLaurentian University
Fundersnot available
KeywordsIdeologyMental illnessIndividualismMainstreamUnemploymentPerspective (graphical)PoliticsState (computer science)Political scienceNeoliberalism (international relations)SociologyMental healthEconomic growthPsychologyPolitical economyEconomicsPsychiatryLaw

Abstract

fetched live from OpenAlex

A variety of explanations have been offered to account for the limited employment of people who experience serious mental illness (SMI) in northeastern Ontario. However, a rights perspective has not been one of them. This paper discusses some of the findings of a larger qualitative case study that may be pertinent to a discussion of rights. While state policy has been largely encouraging participation in employment, people with SMI continue to be marginalized from mainstream employment. In the community studied, more than 91% were experiencing unemployment. The state’s adoption of neoliberal ideology within employment supports programs, with associated ideas of individualism, competition and equality of opportunity, could be argued to marginalize people who experience SMI from employment, limiting their right to employment. Occupational therapists are encouraged to bring a rights perspective to political and public attention, so that employment (and other occupations) becomes a part of the mental health services discourse.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.015
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.076
GPT teacher head0.387
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2010
Admission routes2
Has abstractyes

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Same venueWorld Federation of Occupational Therapists BulletinSame topicMental Health and Patient InvolvementFrench-language works237,207