Medicalization of global health 3: the medicalization of the non-communicable diseases agenda
Bibliographic record
Abstract
There is growing recognition of the massive global burden of non-communicable diseases (NCDs) due to their prevalence, projected social and economic costs, and traditional neglect compared to infectious disease. The 2011 UN Summit, WHO 25×25 targets, and support of major medical and advocacy organisations have propelled prominence of NCDs on the global health agenda. NCDs are by definition 'diseases' so already medicalized. But their social drivers and impacts are acknowledged, which demand a broad, whole-of-society approach. However, while both individual- and population-level targets are identified in the current NCD action plans, most recommended strategies tend towards the individualistic approach and do not address root causes of the NCD problem. These so-called population strategies risk being reduced to expectations of individual and behavioural change, which may have limited success and impact and deflect attention away from government policies or regulation of industry. Industry involvement in NCD agenda-setting props up a medicalized approach to NCDs: food and drink companies favour focus on individual choice and responsibility, and pharmaceutical and device companies favour calls for expanded access to medicines and treatment coverage. Current NCD framing creates expanded roles for physicians, healthcare workers, medicines and medical monitoring. The professional rather than the patient view dominates the NCD agenda and there is a lack of a broad, engaged, and independent NGO community. The challenge and opportunity lie in defining priorities and developing strategies that go beyond a narrow medicalized framing of the NCD problem and its solutions.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".