Medicalization of global health 1: has the global health agenda become too medicalized?
Bibliographic record
Abstract
Medicalization analyses have roots in sociology and have critical usefulness for understanding contemporary health issues including the 'post-2015 global health agenda'. Medicalization is more complex than just 'disease mongering'--it is a process and not only an outcome; has both positive and negative elements; can be partial rather than complete; and is often sought or challenged by patients or others in the health field. It is understood to be expanding rather than contracting, plays out at the level of interaction or of definitions and agenda-setting, and is said to be largely harmful and costly to individuals and societies. Medicalization of global health issues would overemphasise the role of health care to health; define and frame issues in relation to disease, treatment strategies, and individual behaviour; promote the role of medical professionals and models of care; find support in industry or other advocates of technologies and pharmaceuticals; and discount social contexts, causes, and solutions. In subsequent articles, three case studies are explored, which critically examine predominant issues on the global health agenda: global mental health, non-communicable disease, and universal health coverage. A medicalization lens helps uncover areas where the global health agenda and its framing of problems are shifted toward medical and technical solutions, neglecting necessary social, community, or political action.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".