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Record W4313647668 · doi:10.1177/27551938221148376

The Challenge of Exposing and Ending Health Inequalities through Social and Policy Change: Canadian Experiences

2023· article· en· W4313647668 on OpenAlexaffabout
Arnel M. Borras

Bibliographic record

VenueInternational Journal of Social Determinants of Health and Health Services · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsInequalitySocial inequalitySociologyPolitical scienceMathematics

Abstract

fetched live from OpenAlex

The Canadian health system is often perceived as excellent. However, a closer examination of the political economy of health in Canada shows a radically different picture. It is a picture of persistent inequality and a history of the inability to address such inequality. Despite numerous public policy interventions to address preventable health inequalities-that is, health inequities-this societal problem persists. This research addresses how and why health inequities, especially class, race/ethnicity, and gender health inequities, persist in Canada and how to reduce such differences through public policy action. To address these questions, I performed a critical realist review, focusing on the political economy of health and policy change. Then I conducted a thematic analysis of the interview data gathered from 23 semi-structured interviews with leading Canadian policy academics, activists, and advocates. The results demonstrate that the capitalist economic system; the co-constitutives of capitalism, namely colonialism, racism, and sexism; and maldistributive public policies primarily cause health inequities in Canada. Canada's health inequities reduction requires pushing for redistributive public policies; uniting and strengthening labor unions, civil society groups, and social movements; and engaging in electoral politics. Reducing health inequities may involve struggling within and against capitalism and struggling for socialism.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.152
GPT teacher head0.482
Teacher spread0.330 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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