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Record W7036917295

The Cost of Experiencing Everyday Legal Problems Related to Physical and Mental Health

2017· article· en· W7036917295 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2017
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Programs
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthEconomic JusticePublic healthHealth carePer capitaPublic spendingHealth policyHealth promotionAffect (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

In 2016, spending on health care in Canada amounted to approximately $228.1 billion (or an average of $6,299 per person). In several provinces and territories health care spending per capita surpassed $7,000. These amounts represent a significant economic commitment to spending on health care. And while many of the costs associated with maintaining and providing health care are unavoidable, there are also many external factors that adversely affect physical and mental health that, if addressed, can offer avenues for reduced spending and contribute to quality of life. This summary report explores the relationship between health issues, civil and family justice problems in Canada and public spending on health care. Using findings from the Canadian Forum on Civil Justice’s (CFCJ) national Everyday Legal Problems and the Cost of Justice in Canada study this report will look at different civil justice problem types, identify trigger problems and explore an often overlooked factor that impacts physical and mental health and public spending on health care in Canada: civil and family justice problems.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.037
GPT teacher head0.351
Teacher spread0.314 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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