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Record W2411249360

Opening Minds: The Mental Health Commission of Canada’s Anti-Stigma Initiative: Opening Minds in Canada: Background and Rationale

2014· article· en· W2411249360 on OpenAlexvenueaboutno aff
Heather Stuart, Shu‐Ping Chen, Romie Christie, Keith S. Dobson, Bonnie Kirsh, Stephanie Knaak, Michelle Koller, Terry Krupa, Bianca Lauria-Horner, Dorothy Luong, Geeta Modgill, Scott B. Patten, Mike Pietrus, Andrew C. H. Szeto, Rob Whitley

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthCommissionMandateStigma (botany)AddictionPolitical sciencePsychiatryMedicinePsychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

In 2006, Canada’s Senate Committee on Social Affairs, Science and Technology completed a national review of mental health and addiction services in Canada1—the first national review since the report of the Royal Commission on Psychiatric Services published almost half a century earlier.2 The Committee recommended that a mental health commission be created, which was subsequently established in 2007 with the full support of all federal parties. The MHCC was funded through Health Canada, with a 10-year mandate to act as a catalyst for improving the mental health system and changing the attitudes and behaviours of Canadians regarding mental health issues. The OM Anti-Stigma Initiative of the MHCC was launched on October 2, 2009. Our paper will provide the rationale for the approach taken and summarize the way in which programs were identified and engaged in this initiative.

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)0.024
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score0.840

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0330.031
Scholarly communication0.0160.005
Open science0.0060.012
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0050.000

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 head0.286
GPT teacher head0.503
Teacher spread0.217 · 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 designNot applicable
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

Citations2
Published2014
Admission routes2
Has abstractyes

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