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Record W1579889869

Charter rights & health care funding: a typology of Canadian health rights litigation.

2010· article· en· W1579889869 on OpenAlexaffabout
Colleen M. Flood, Y.Y. Brandon Chen

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsInstitute of Health Services and Policy ResearchUniversity of Ottawa
Fundersnot available
KeywordsCharterTypologyRight to healthGovernment (linguistics)Health carePublic administrationLawsuitBusinessPolitical scienceFundamental rightsLaw and economicsHuman rightsLawSociology
DOInot available

Abstract

fetched live from OpenAlex

Canadian health consumers have increasingly relied on the Charter of Rights and Freedoms to demand certain therapies and reasonably timely access to care. Organizing these cases into a 5-part typology, we examine how a rights-based discourse affects allocation of health care resources. First, successful Charter challenges can, in theory, lead to courts granting and enforcing positive rights to therapies or to timely care. Second, courts may grant a right to certain health services; however, subsequently government fails to deliver on this right. Third, successful litigation may create negative rights, i.e. rights to access care or private health insurance without government interference. Fourth, consumers can fail in their legal pursuit of a right but galvanize public support in the process, ultimately effecting the desired policy changes. Lastly, a failed lawsuit can stifle an entire advocacy campaign for the sought-after therapies. The typology illustrates the need to examine both legal and policy outcomes of health right litigation. This broader analysis reveals that the pursuit of health rights seems to have caused largely a regressive rather than progressive impact on Canadian Medicare.

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)0.020
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 score0.845
Threshold uncertainty score0.980

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0090.011
Science and technology studies0.0310.037
Scholarly communication0.0130.007
Open science0.0030.005
Research integrity0.0090.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.070
GPT teacher head0.390
Teacher spread0.320 · 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

Citations4
Published2010
Admission routes2
Has abstractyes

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