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Dual Diagnosis Public Policy in a Federal System: The <scp>C</scp>anadian Experience

2012· article· en· W1786456074 on OpenAlexaff
Heather Gough, Susan Morris

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2012
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsLegislationStatuteRatificationContext (archaeology)LegislatureGovernment (linguistics)Public policyDual (grammatical number)Mental healthHealth carePublic administrationMedicineBusinessPolitical sciencePsychiatryLawPolitics

Abstract

fetched live from OpenAlex

Abstract Persons with dual diagnosis, defined as having both mental health needs and developmental disabilities, can have significant problems accessing appropriate and integrated care. It was hypothesized that jurisdictions across C anada would be lacking in legislation and policy regarding care for dual diagnosis. A legislative scan was performed, encompassing statutes and regulations, followed by a search of government ministerial Web sites to identify policy; relevant persons within ministries were contacted for clarification and further information. Findings indicated that no province or territory within C anada currently has legislation regarding dual diagnosis; four have policies addressing dual diagnosis care. Some policies exclude people with disabilities from qualifying as having a psychiatric disorder, and some policies exclude people with certain types of psychiatric disorders. The patchwork of regional legislation and policy in C anada suggests that dual diagnosis care would be better facilitated if rights to such were enshrined within legislation. Ratification by C anada of the UN C onvention on the R ights of P ersons with D isabilities and a national mental health strategy provide a new policy context that is worth monitoring in relation to rectifying the current situation.

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)0.004
metaresearch head score (Gemma)0.354
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.354
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.002
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 head0.110
GPT teacher head0.436
Teacher spread0.326 · 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 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

Citations10
Published2012
Admission routes1
Has abstractyes

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