COMMENTARY Identifying and Addressing the Social Determinants of the Incidence and Successful Management of Type 2 Diabetes
Bibliographic record
Abstract
There is an increased awareness that health can be profoundly affected by a myriad of social, environmental and economic factors and that good health means more than just good physical health.1 The statement above from the Canadian Diabetes Association illustrates howCanadian health researchers and policy-makers are recognizing the importance ofvarious social determinants of population health.2-4 These factors include income and its distribution, housing and food security, and the quality of physical and social envi-ronments.5,6 But diabetes-related research on its causes and prevention activities continues to be focussed on biomedical and lifestyle risk factors with little if any attention given to these broader issues.7,8 The same appears to hold true when issues of management and con-trol are explored. In the case of type 2 diabetes (referred to as diabetes in this paper) – the most prevalent form of the disease9,10 and the centre of attention of the “diabetes epidemic” – disease associations, public health, and other health workers continue to espouse the ben-efits of appropriate diet, adequate exercise, and weight control as a population-wide strate-gy in its prevention and management.10-14 Much of the focus of prevention and management is highly medicalized.13 High-risk individuals are urged to seek medical attention; a physician or other allied health profes-
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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 | 0.011 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.043 | 0.028 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".