Tackling Health Inequalities through Public Policy Action: Insights from Canadian Policy Academics, Activists, and Advocates
Bibliographic record
Abstract
Despite numerous public policy proposals and interventions to address preventable health inequalities, that is, health inequities among and within countries, 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. First, I performed a theoretical and critical realist review of existing literature focusing on pluralism, discursive institutionalism, and critical political economy approach to health and policy change. Then I conducted a thematic analysis of the interview data corpus gathered from 23 semi- structured interviews with leading and influential Canadian policy academics, activists, and advocates to address the research questions. Reflexivity also forms part of my methods.\n\nThe main findings demonstrated that health inequities or the avoidable health inequalities in Canada are primarily caused by 1) the capitalist economic system; 2) the co-constitutives of capitalism, namely colonialism, racism, and sexism; and 3) maldistributive public policies. Health inequities are further sustained by 1) power, interest, and ideology trumping evidence-based research and policy ideas; 2) unequal wealth and power among competing interests and advocacy groups; 3) the dominance of the business and corporate sector in health politics and public policymaking processes; 4) neoliberal governing authorities; and 5) fragmented and weak labour unions, civil society groups, and social movements.\n\nCanadas health inequities reduction efforts necessitate 1) pushing for redistributive public policies; 2) uniting and strengthening labour unions, civil society groups, and social movements; and 3) engaging in electoral politics. The core strategies to realize these health equity goals are the ensemble of information, education, advocacy, organization, and mobilization. Reducing health inequities in general and class, race/ethnicity, and gender health inequities, in particular, may involve struggling within and against capitalism and struggling for socialism. This study may provoke social actions toward emancipatory social change to achieve health justice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.069 | 0.042 |
| Scholarly communication | 0.027 | 0.006 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".