Closing the health gap among Canadians: Using Co-Active Life Coaching to address the challenges to primary healthcare
Bibliographic record
Abstract
Several studies have reported the burden of obesity and cardiometabolic risk to be disproportionately greater among ethnic minorities in comparison to those of European-descent.1,2 Generally, the 'healthy immigrant effect' is proposed as the reason for this disparity, in which new immigrants start to lose their health advantage when they start to adopt the physical, social and cultural environment of their newly adopted country. 1 However, evidence suggests that aboriginal populations also experience poorer obesity-related health outcomes relative to the entire Canadian population, 3 which suggests that this health inconsistency may not be exclusive to immigrant populations.Additionally, this may reflect a deeper issue of accessibility and utilization of primary care services among Canada's diverse population.The following paper will explore the barriers to healthcare access and utilization, specifically primary care among ethnic minorities.In addition, this paper aims to highlight a potential health behaviour intervention, known as Co-Active Life Coaching (CALC), which may serve as a means to manage obesity and cardiometabolic risk and ultimately, alleviate the high demand for primary care.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".