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Record W2903039150 · doi:10.3233/wor-182826

Accounting for context: Social enterprises and meaningful employment for people with mental illness

2018· article· en· W2903039150 on OpenAlexaff
Robert Wilton, Joshua Evans

Bibliographic record

VenueWork · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsMental illnessMandateMainstreamPublic relationsContext (archaeology)Supported employmentBusinessMental healthWork (physics)PsychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Many people living with mental illness want paid work, but finding and maintaining mainstream employment remains challenging. In recent decades, social enterprises have emerged as one alternative site for paid employment. Existing research has examined the experiences of people with mental illness working in social enterprises, but less is known about the organizational character of these workplaces. OBJECTIVE: The objective of this paper is to develop a better understanding of social enterprises as organizational contexts for workers with mental illness. METHODS: The research employed a qualitative methodology, conducting semi-structured interviews with executive directors and managers at 42 organizations operating 67 social enterprises across CanadaRESULTS:While there are strong similarities in organizational mandate to create meaningful employment there are also important variations between social enterprises. These include variations in size, economic activity and organizational structure, as well as differences in hours of work, rates of pay and the nature and extent of workplace accommodation. These variations reflect both immediate organizational contexts as well as broader economic constraints that enterprises confront. CONCLUSIONS: Understanding the varied nature of social enterprises is important for thinking about future enterprise development, and the capacity of such organizations to create meaningful employment for people living with mental illness.

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 score0.308
Threshold uncertainty score0.290

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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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