Chronic Disease Prevention and Management: Implications for Health Human Resources in 2020
Bibliographic record
Abstract
Through improved screening, detection, better and more targeted therapies and the uptake of evidence-based treatment guidelines, cancers are becoming chronic diseases. However, this good-news story has implications for human resource planning and resource allocation. Population-based chronic disease management is a necessary approach to deal with the growing burden of chronic disease in Canada. In this model, an interdisciplinary team works with and educates the patient to monitor symptoms, modify behaviours and self-manage the disease between acute episodes. In addition, the community as a whole is more attuned to disease prevention and risk factor management. Trusted, high-quality evidence-based protocols and healthy public policies that have an impact on the entire population are needed to minimize the harmful effects of chronic disease. Assuming we can overcome the challenges in recruitment, training and new role development, enlightened healthcare teams and community members will work together to maintain the population's health and wellness and to reduce the incidence and burden of chronic disease in Ontario.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 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".