Bibliographic record
Abstract
OntarioMD (OMD), a leader in digital health, focuses on harnessing artificial intelligence (AI) technologies to reduce administrative burden and enhance patient care in primary care. Building on 20 years of digital health experience, OMD has established an AI implementation strategy centred on collaboration and education. This multipronged strategy leads to a sustainable, effective and safe adoption of AI through collaboration with healthcare providers, patients, policymakers, technology vendors, and regulatory bodies by offering implementation toolkits and change management support to clinicians and fostering a culture of continuous learning, ensuring clinicians and patients are well-versed in AI. They are equipped with the necessary knowledge to leverage the AI-enabled tools. The success of the AI scribe pilots in Ontario exemplifies the value of this AI implementation strategy. Clinicians who participated in the pilot reported saving almost 4 hours per week on documentation by using AI scribes, and both primary care providers and patients reported improved engagement and rapport.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".