Global health activists’ lessons on building social movements for Health for All
Bibliographic record
Abstract
Abstract Background The People’s Health Movement (PHM) formed in 2000 and drew inspiration from the Alma Ata Declaration on Primary Health Care’s ‘Health for All’ (1978). Since then PHM has been an active part of a global counter-hegemonic social movement. From locations across the world including eleven countries, health activists shared their insights on social movement building, drawing on the successes and failures over many decades of striving to improve health, justice and equity. Methods Qualitative research methods were employed in this study to capture complex and historical narratives of individual activists, including semi-structured interviews and thematic analysis of transcripts. The research design and analysis were informed by social movement theory and literature on health activism as a pathway for social change. In this paper we examine the semi-structured interviews of 15 health activists who are part of the PHM, to derive lessons for strengthening movements for Health for All. Results This paper locates the activists’ narratives within a socio-political analysis of the global trends of late modern individualism and capitalist neoliberalism to understand the challenges faced by civil society groups mobilising collective action and building social movements for Health for All. This study found that within the constraints of the neoliberal socio-political and economic conditions which have caused the rise in social and health inequities, this group of long-term health activists have been nurturing alternative approaches to structuring society and building collective agency to improve health. Conclusion This paper draws on the practical long-term experiences of the PHM activists to understand better the processes and motivations that lead to and sustain health activism, and the dilemmas, strategies, impacts and achievements of such activism.
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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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".