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Record W2889063252 · doi:10.7870/cjcmh-2017-033

Mental Health and Primary Care: Contributing to Mental Health System Transformation in Canada

2017· article· en· W2889063252 on OpenAlexaffvenueabout
Nick Kates

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMental healthCollaborative CareWork (physics)Health careNursingPrimary careCommissionPlan (archaeology)Mental health careMedicinePsychologyBusinessPolitical scienceFamily medicinePsychiatryEngineeringGeography

Abstract

fetched live from OpenAlex

For 20 years mental health and primary care providers across Canada have been working collaboratively together to improve access to care, provider skills, and patient experience. The new strategic plan of the Mental Health Commission of Canada (MHCC) offers many opportunities for collaborative care to play a role in the transformation of Canada’s mental health systems. To assist the plan, this paper presents principles underlying successful projects and ways that mental health and primary care services can work together more collaboratively, including integrating mental health providers in primary care. It integrates these concepts into a Canadian Model for Collaborative Mental Health Care that can guide future expansion of these approaches, and suggests ways in which better collaboration can address wider issues facing all of Canada’s health care systems.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.354
Teacher spread0.315 · 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

Citations30
Published2017
Admission routes3
Has abstractyes

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