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Record W1980038721 · doi:10.1097/yco.0b013e32833bb305

Social inclusion and mental health

2010· review· en· W1980038721 on OpenAlexaff
Virginie Cobigo, Heather Stuart

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2010
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsQueen's University
Fundersnot available
KeywordsInclusion (mineral)Mental healthPopularityContext (archaeology)LegislationLegislaturePublic relationsPsychologyPolitical scienceSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Recent research on approaches to improving social inclusion for people with mental disabilities is reviewed. RECENT FINDINGS: We describe four approaches (or tools) that can be used to improve social inclusion for people with mental disabilities: legislation, community-based supports and services, antistigma/antidiscrimination initiatives, and system monitoring and evaluation. While legislative solutions are the most prevalent, and provide an important framework to support social inclusion, research shows that their full implementation remains problematic. Community-based supports and services that are person-centered and recovery-oriented hold considerable promise, but they are not widely available nor have they been widely evaluated. Antistigma and antidiscrimination strategies are gaining in popularity and offer important avenues for eliminating social barriers and promoting adequate and equitable access to care. Finally, in the context of the current human rights and evidence-based health paradigms, systematic evidence will be needed to support efforts to promote social inclusion for people with mental disabilities, highlight social inequities, and develop best practice approaches. SUMMARY: Tools that promote social inclusion of persons with mental disabilities are available, though not yet implemented in a way to fully realize the goals of current disability 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.364
GPT teacher head0.577
Teacher spread0.213 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations93
Published2010
Admission routes1
Has abstractyes

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