A Collaborative Approach to a Chronic Care Problem: An Academic Mentor’s Point of View
Bibliographic record
Abstract
The Atlantic Healthcare Collaboration for Innovation and Improvement in Chronic Disease (AHC) represents a social experiment of sorts. The AHC provided a platform to integrate regions, health issues, healthcare systems, providers and individuals/families living with chronic disease. As such, the scope of the AHC was very broad, providing a rich learning environment but also risking biting off more than it could chew. I participated in this experiment as an academic mentor to three of the improvement projects (IPs) with Health PEI, Central Health and Western Health and also was a member of the IP extended team at Nova Scotia Health Authority (formerly Capital Health) in Nova Scotia. My professional contribution was from the perspective of health behaviour change - change at the level of the patient and family living with chronic disease, at the level of the healthcare provider working within an expert-based, siloed system, and at the level of the healthcare system - the managers and decision-makers.
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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.041 | 0.067 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".