Improving policy and practice to promote equity and social justice – a qualitative comparative analysis building on key learnings from a twinning exchange between England and the US
Bibliographic record
Abstract
Community health promotion interventions, targeted at marginalised populations and focusing on addressing the social determinants of health (SDH) to reduce health inequalities and addressing the processes of exclusion, are an important strategy to prevent and control non-communicable diseases (NCDs) and promote the health of underprivileged and under-resourced groups. This article builds on key lessons learnt from a learning exchange between Communities for Health in England and the Racial and Ethnic Approaches to Community Health across the US (REACH US) communities that are tackling health inequities. It presents a qualitative analysis further capturing information about specific community interventions involved in the exchange and identifying lessons learnt. This exchange was led by a partnership between the US Centers for Disease Control and Prevention, the International Union for Health Promotion and Education, the Department of Health of England, Health Action Partnership International, and Learning for Public Health West Midlands. These efforts provide interesting insights for further research, priority areas of action for policy and practice to address the SDH and to promote and sustain equity and social justice globally. The article highlights some key lessons about the use of data, assets-based community interventions and the importance of good leadership in times of crisis and adversity. Whilst complex and time-consuming to arrange, such programmes have the potential to offer other countries including the global south new insights and perspectives that will in turn contribute to the SDH field and provide concrete strategies and actions that effectively reduce inequities and promote the health of our societies. The key learnings have the potential to contribute to the global community and growing documentation on evidence of effective efforts in the reduction of health inequities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".