The Prevention of Global Chronic Disease and Academia: Another Key Area?
Bibliographic record
Abstract
The recent analytic essay by Greenberg et al. rightfully emphasized the role of the public health academia in preventing global chronic disease.1 The authors focused on key areas such as resource allocation and overcoming barriers to prevention, but paid less attention to academia's crucial role in advancing fresh thinking or innovative models to tackle the epidemic. Two such emerging concepts are Complexity Science (CS) and Allostatic Load (AL) in the causation of chronic diseases.2–5 Our worldview is shifting from mechanistic models to the new CS paradigm. CS perceives social systems (e.g., financial markets) and natural systems (e.g., population health) as emergent properties of open, dynamic, nonlinear, and adaptive systems (i.e., Complex Adaptive Systems [CASs]). Patterns of health and chronic diseases are conceptualized as emergent properties of populations.3 These patterns arise from networks of interactions among dynamic sets of interconnected nonlinear subsystems (e.g., political systems, physical and social environments) that predominantly reside in the latter.6,7 To facilitate changes in a CAS (i.e., health of a population) multiple interventions are required in several subsystems and sectors rather than one magic bullet such as vaccines. Interventions have to be flexible, constantly monitored, and evaluated, and information must be fed back to change the course of intervention. There are few documented reports of CS in health interventions, and a recent report describes its application in an attempt to reduce cardiovascular diseases morbidity and mortality among Canadian Asians.8 Exploring CASs in health will deepen our understanding of how social systems, climate change, human behaviors, and physical environments promote development of chronic diseases in humans and will help us tackle this issue. AL denotes the cumulative wear and tear or the physiological toll experienced by the body over a life course to adapt to biological, psychological, and environmental demands to maintain homeostasis.4 It postulates that chronic diseases are mediated through stress pathways and suggests that stressors of daily living play a role in promoting chronic diseases rather than stemming from a limited set of risk factors.9 Thus, an ordinary social life (as we understand it) may promote chronic diseases over and above what can be explained by the extended life span and traditional risk factors. Because chronic disease affects a majority of the global population, the burden can be substantial. These are just two examples of how fresh thinking and innovative approaches may help us to unravel, understand and contain the epidemic of chronic diseases.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.025 | 0.058 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.024 | 0.040 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".