Why Social (Political, Economic, Cultural, Ecological) Determinants of Health? Part 1: Background of a Contested Construct
Bibliographic record
Abstract
This article is the first half of a 2-part essay on the Social Determinants of Health (SDOH) as a field of scientific inquiry and theoretical framework, exploring its historical roots, current applications, and the controversies that surround it. Part 1 (this article) discusses the background and rationale of the SDOH framework, whilst part 2 (forthcoming) will analyze the current alternatives to this framework. The authors analyze the debate surrounding the contested term “social” in the field of health equity, through a clarification of the terms “social” and “social systems” and providing an alternative model through realist semantics and ethics. Despite the misunderstandings of the term “social,” the authors argue that SDOH remains a useful umbrella term to capture the political, economic, cultural, and ecological determinants of health. Through this essay, the authors outline the reasons behind our decision to change this journal's title from International Journal of Health Services to International Journal of Social Determinants of Health and Health Services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".