Medicalization of global health 3: the medicalization of the non-communicable diseases agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it