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Record W3045596450

Fragmented Law & Fragmented Lives: Canada’s Mental Health Care System

2017· article· en· W3045596450 on OpenAlexaffabout
Colleen M. Flood, Bryan Thomas

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsMental healthHealth lawMedical prescriptionMental health lawHealth careMental health careMedicineNursingPolitical scienceBusinessHealth policyLawInternational healthPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

This chapter focuses on the role of law in shaping Canada’s mental health care system and perpetuating failed policy choices, leading to serious access problems for those in need of mental health care. We begin with a general discussion of the funding of Canada’s mental health care system, focusing on a lack of universal coverage for psychological counseling, prescription drugs, home care and other community supports. Next we examine how the law, particularly the present practice in interpreting the constitutional division of powers, impacts the funding of mental health services and supports a fragmented delivery system. We then review gaps in the delivery of primary care services (including prescription drugs), hospital services and community care services. We also discuss particular failings in our present system for the treatment of children with mental health care needs. Having traced the legal underpinnings for the fragmentation of Canadian mental health services, we end by exploring how law (both in terms of law reform and litigation) could play a more positive role in improving the Canadian mental health care system.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.654
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0430.022
Scholarly communication0.0180.005
Open science0.0040.008
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0080.000

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 head0.007
GPT teacher head0.270
Teacher spread0.263 · 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 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

Citations3
Published2017
Admission routes2
Has abstractyes

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