Predicting non-communicable disease based on behavioral risk factors and social determinants of health -- A Canadian study
Bibliographic record
Abstract
In Canada, the concern over non-communicable diseases (NCDs), also known as chronic diseases, has become a major issue. With the significant increase of NCDs risk factors, Canadians are facing NCDs challenges. Two out of five Canadians, above the age of 12 years, have at least one NCD and 80% are at risk of developing a NCD. NCDs rates are affected by a complex interaction of factors including the underlying biological, behavioral, social, physical conditions, and health service related. This study only focused on behavioral and social conditions. The impact of social determinants of health (SDOH) and behavioral risk factors (BRFs) on individualsâ probability of getting a NCD in Canada were assessed in this study. Both the independent effect of each risk factor and their multi-function effects were assessed. BRFs include unhealthy diet (low fruit and vegetable consumption), physical inactivity, tobacco use and harmful use of alcohol. The SDOH considered in this thesis include income, education level, marital status, age and work stress. The Canadian Community Health Survey (2010) data set was used in the analysis of this study. A sample of 62,909 individuals were investigated in the CCHS 2010 survey. Univariable and multivariable logistic regression models were used in the analysis. These results indicate that the socio-economic status is related to an individualâs probability of getting a NCD. Higher socio-economic status is associated with better health, and people with less NCDs. Respondents who reported higher levels of education and income experienced fewer NCDs than respondents with lower education and income levels. Respondents with the highest work stress levels were more likely to have NCDs than those not so stressed. A healthy lifestyle, i.e. more fruit and vegetable consumption, being physically active, less smoking, is important to maintain better physical health in order to reduce the risk of having a NCD. Respondents who were obese and overweight were more likely to have NCD than those of normal weight. However, due to limitations of the data, the results regarding marital status and alcohol consumption were not clear and needs further research. Recommendations from both institutional level and community involvement policies were made in the conclusion chapter of the thesis.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".