Physician Assistants as Chronic Care Coordinators - An Interdisciplinary Patient Centered Approach to Managing Diabetes
Bibliographic record
Abstract
With an aging population and an increasing number of people living with chronic disease, Canada’s primary care system is in need of change. Healthcare must better incorporate prevention and patient education in the battle against chronic disease. This article exploresthe growing role of Physician Assistants (PAs) in enhancing access to appropriate care for the chronically ill, using an example of a PA working as part of a family physician practice in Northern Ontario to improve the care of its diabetic patients. Avec une population vieillissante et un nombre croissant de personnes vivant avec une maladie chronique, le système de soins primaires canadien est en besoin de changement. Les soins de santé doivent mieux intégrer la prévention et l’éducation des patients dans la lutte contre les maladies chroniques. Cet article explore le rôle croissant des adjoints au médecin (AM) dans l’amélioration de l’accès aux soins appropriés pour les patients vivants avec des malades chroniques. Ceci sera illustré par le biais d’un exemple d’un AM travaillant dans une pratique de médecine familiale au Nord de l’Ontario pour améliorer les soins de ses patients diabétiques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".