Why language matters: insights and challenges in applying a social determination of health approach in a North-South collaborative research program
Bibliographic record
Abstract
BACKGROUND: Focus on "social determinants of health" provides a welcome alternative to the bio-medical illness paradigm. However, the tendency to concentrate on the influence of "risk factors" related to living and working conditions of individuals, rather than to more broadly examine dynamics of the social processes that affect population health, has triggered critical reaction not only from the Global North but especially from voices the Global South where there is a long history of addressing questions of health equity. In this article, we elaborate on how focusing instead on the language of "social determination of health" has prompted us to attempt to apply a more equity-sensitive approaches to research and related policy and praxis. DISCUSSION: In this debate, we briefly explore the epistemological and historical roots of epidemiological approaches to health and health equity that have emerged in Latin America to consider its relevance to global discourse. In this region marked by pronounced inequity, context-sensitive concepts such as "collective health" and "critical epidemiology" have been prominent, albeit with limited acknowledgement by the Global North. We illustrate our attempts to apply a social determination approach (and the "4 S" elements of bio-Security, Sovereignty, Solidarity and Sustainability) in five projects within our research collaboration linking researchers and knowledge users in Ecuador and Canada, in diverse settings (health of healthcare workers; food systems; antibiotic resistance; vector borne disease [dengue]; and social circus with street youth). CONCLUSIONS: We argue that the language of social determinants lends itself to research that is more reductionist and beckons the development of different skills than would be applied when adopting the language of social determination. We conclude that this language leads to more direct analysis of the systemic factors that drive, promote and reinforce disparities, while at the same time directly considering the emancipatory forces capable of countering negative health impacts. It follows that "reverse innovation" must not only recognize practical solutions being developed in low and middle income countries, but must also build on the strengths of the theoretical-methodological reasoning that has emerged in the South.
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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 | 0.342 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.053 | 0.115 |
| Scholarly communication | 0.049 | 0.055 |
| Open science | 0.009 | 0.044 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".