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Record W1035268818 · doi:10.3233/wor-2012-1452

Enabling careers, autonomy, and prosperity: Using community organizing and building approaches to improve the educational outcomes of people with mental illness

2012· article· en· W1035268818 on OpenAlexaff
Terry Krupa, Glenda Carter

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeneral partnershipMental healthMental illnessAutonomyPublic relationsProsperityService providerService (business)Medical educationNursingPsychologyPolitical scienceMedicineBusinessPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this regional initiative was to develop access to educational opportunities for people with mental illness with a view to ultimately advancing their career prospects. PARTICIPANTS: The initiative engaged a broad range of community stakeholders including people with mental illness, their families, educators, mental health service providers and, policy analysts. METHODS: The initiative used community organizing and development strategies to develop solutions to problems related to access to education. RESULTS: The initiative was successful in mobilizing community participation, identifying priorities, and translating these priorities into action plans. Working groups of community stakeholders engaged in initiatives related to improving access to resources to support education, developing training for teachers in secondary schools, creating peer support systems, and developing a pilot supported education program as a partnership between a college and mental health service. CONCLUSION: Organized community building provided a foundation for a broad range of initiatives meant to improve access to educational opportunities for people with mental illness. Evaluation efforts will need to focus on the extent to which these initiatives ultimately ledto positive changes in the careers of people 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 score0.010
Threshold uncertainty score0.244

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 head0.068
GPT teacher head0.294
Teacher spread0.226 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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