Advancing a health equity agenda across multiple policy domains: a qualitative policy analysis of social, trade and welfare policy
Bibliographic record
Abstract
OBJECTIVE: While there is urgent need for policymaking that prioritises health equity, successful strategies for advancing such an agenda across multiple policy sectors are not well known. This study aims to address this gap by identifying successful strategies to advance a health equity agenda across multiple policy domains. DESIGN: We conducted in-depth qualitative case studies in three important social determinants of health equity in Australia: employment and social policy (Paid Parental Leave); macroeconomics and trade policy (the Trans Pacific Partnership agreement); and welfare reform (the Northern Territory Emergency Response). The analysis triangulated multiple data sources included 71 semistructured interviews, document analysis and drew on political science theories related to interests, ideas and institutions. RESULTS: Within and across case studies we observed three key strategies used by policy actors to advance a health equity agenda, with differing levels of success. The first was the use of multiple policy frames to appeal to a wide range of actors beyond health. The second was the formation of broad coalitions beyond the health sector, in particular networking with non-traditional policy allies. The third was the use of strategic forum shopping by policy actors to move the debate into more popular policy forums that were not health focused. CONCLUSIONS: This analysis provides nuanced strategies for agenda-setting for health equity and points to the need for multiple persuasive issue frames, coalitions with unusual bedfellows, and shopping around for supportive institutions outside the traditional health domain. Use of these nuanced strategies could generate greater ideational, actor and institutional support for prioritising health equity and thus could lead to improved health outcomes.
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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 | 0.051 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".