Tackling health inequalities : lessons from international experiences
Bibliographic record
Abstract
This book provides a unique perspective on health inequalities in Canada and elsewhere. This exciting new volume brings together experiences from seven wealthy developed nations -- the United States, Australia, Britain and Northern Ireland, Canada, Finland, Norway, and Sweden -- to analyse their contrasting approaches to reducing avoidable health problems. Some nations are successfully responding to health inequalities, but Canada is not one of them. Why is this, and what can we learn from other nations? Through a political economy lens, this book considers how societal structures and institutions shape the distribution of economic, political, and social resources that affect health disparities amongst the population. The volume then goes on to examine how governing authorities come to either confront or ignore these health inequalities and the conditions that create them. Through these illustrations, it encourages governing authorities that are tackling health inequalities to continue their efforts and directs those that are not -- such as in Canada and elsewhere -- towards what must be done. This ground-breaking text shows the primary lessons from these international experiences: that citizens in Canada and elsewhere need to educate themselves about the importance of tackling health inequalities, and then build the political and social movements that will compel governmental authorities to take action. This volume will serve as a rich resource for professionals and general readers interested in health studies, nursing, social work, public policy, and political economy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.040 | 0.020 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".