Achieving Health Equity for All Canadians: Is AI Currently Up to the Task?
Bibliographic record
Abstract
Artificial intelligence (AI) deployed into healthcare settings is touted as an exciting approach for improving health equity. However, several issues need to be addressed before this could be achieved, including improving the collection and use of the social determinants of health data, enhancing data interoperability, closing the digital divide and conducting rigorous assessment and evaluation of AI applications to ensure that they achieve fair and equitable outcomes in real-world settings. Importantly, we should not neglect evidence-based strategies that will truly advance health equity, such as adequate housing, poverty reduction, accessible mental healthcare, food security and many other structural and social determinants of health.
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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 | 0.021 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.032 | 0.034 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".