The Case for Collaboration in Health Infostructure Developments; Can We Deliver on the Promise IT Holds?
Bibliographic record
Abstract
At the outset of its health reforms in 1992, Saskatchewan identified health information and technology as a key enabler for healthsystem change. Collaboration with the new health districts led to a provincial health IT architecture and development of an armslength agency, the Saskatchewan Health Information Network (SHIN) to operationalize this vision. Drawing from experience in Saskatchewan, this article explores both the benefits and challenges of collaboration at a time when planning and investment through a new pan-Canadian approach to health infostructure holds out so much promise and opportunity for health informatics in our country. SASKATCHEWAN’S EXPERIENCE As the birthplace of Medicare it is perhaps not too surprising that Saskatchewan was one of the first provinces to restructure the delivery of most of its health services on an integrated community-based model. Health reform, as it was known at the time, began in Saskatchewan in 1992. Over 450 healthcare institutions, including individual hospitals, special care homes and home care facilities, were replaced with 32 new health districts as an initial step in this process. Saskatchewan Health, in early planning for these sweeping changes to our health system, recognized that health information and the information technologies would play an important supporting role in enabling the new vision for health in the province to become a reality. Saskatchewan
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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 | 0.117 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.070 |
| Scholarly communication | 0.051 | 0.068 |
| Open science | 0.006 | 0.048 |
| Research integrity | 0.033 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 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".