Canada’s Shared Health Priorities: Measuring Progress and Bridging Data Gaps With Common Indicators
Bibliographic record
Abstract
In 2023, Canada's federal, provincial and territorial governments agreed to work together to improve healthcare across four priority areas and to develop common indicators to measure progress and report back to Canadians. In October 2024, the Canadian Institute for Health Information released Taking the Pulse: Measuring Shared Priorities for Canadian Health Care, 2024, which provides baseline results for 12 of these indicators. Some of the key findings include the following: Eighty-three percent of Canadians report having access to a regular healthcare provider. Half of Canadians referred to publicly funded community mental health counselling waited 25 days or less for their first scheduled session. Only two in five Canadians have accessed their personal health information electronically.
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 | 0.177 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.028 | 0.046 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".