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Record W2143243202 · doi:10.1080/713658246

Health inequalities in Canada: Current discourses and implications for public health action

2000· article· en· W2143243202 on OpenAlexaffabout
Dennis Raphael

Bibliographic record

VenueCritical Public Health · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInequalityPovertyPublic healthSocial inequalityHealth equitySocial determinants of healthEconomic growthPolitical scienceDevelopment economicsEconomicsHealth careMedicine

Abstract

fetched live from OpenAlex

Data concerning increasing economic inequality and its effects are increasingly becoming available in Canada. Warnings concerning the consequences of increasing economic inequality are primarily being raised within the social development sectors. The primary message is that economic inequality is creating poverty, a situation that should, on principle, be unacceptable to Canadians. The health effects of economic inequality and poverty are known to many public health professionals, but with few exceptions, public health responses are usually limited to the delivery of ameliorative programmes to those living in poverty. While federal, some provincial, and public health association documents include economic inequality as a determinant of health, discussions of the role that economic inequality plays in creating poverty, its impact upon community structures that support health, and the causes of increasing inequality are for the most part, isolated from public health discourse. Evidence of, and reasons for, resistance to such analyses and potential courses of action for addressing economic inequality and its health effects are presented.

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)0.015
metaresearch head score (Gemma)0.032
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 score0.280
Threshold uncertainty score0.835

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.017
Science and technology studies0.0420.036
Scholarly communication0.0260.009
Open science0.0050.011
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0090.000

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 head0.271
GPT teacher head0.491
Teacher spread0.221 · 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

Citations72
Published2000
Admission routes2
Has abstractyes

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