Public health issues and non-communicable diseases in Saskatchewan, Canada
Bibliographic record
Abstract
Objective: Main purpose of this paper was to report the occurrence of chronic non-communicable diseases such as diabetes, obesity, smoking, alcohol consumption and roadside injuries and its impact on the Saskatchewan healthcare system. Methods: We searched the Public Health Agency of Canada, Canadian Institute for Health Information website, electronic databases including Medline, Scopus, PubMed, and CINAHL to review published literature in this area. Electronic searches were limited to Health care, Diabetes, Obesity, Alcohol and Roadside injuries in Saskatchewan. Results: Obesity is associated with diabetes and hyperlipidemia. Insufficient fruit and vegetable intake may be associated with obesity but still there is a dire need of more research to understand of obesity. Smoking leads to chronic respiratory diseases, coronary artery disease and cancer, making it the leading cause of death. Impaired driving related injuries are the leading consequences of alcohol which is burdening the nation with millions of dollars annually. There is a need for an effective mass media campaign to develop awareness. Overall hospitalization due to traffic-accidents is on the decline nationally due to effective safety measures and enhancement of quality care. However, traffic accidents are still the leading cause of admission due to unintentional injuries. Conclusions: The key issues that needs to be addressed as a priority are those related to diabetes, obesity, alcohol and respiratory diseases that result in serious threat to the public health of Saskatchewan residents. Finally, promoting healthy living and positive behaviour can prevent incidence of these chronic conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".