Bibliographic record
Abstract
is an important piece of legislation aimed at safeguarding an individual's right to control their personal health information. Since this time, the world of data and analytics has shifted in terms of our potential to collect, integrate, and analyze both structured and unstructured data. The implications for these data advancements are endless for our healthcare system; however, challenges influenced by our approach to collecting, accessing, and analyzing data as well as patient consent to share personal health information mean public entities lag behind commercial players in harnessing these potential benefits. While there are examples of data analytics application successes, Canadian healthcare continues to lag behind other countries and commercial sectors. We are at a pivot point for system improvements requiring a collective approach to collection, storage, linkage, and application of personal healthcare data. In the chasm of this rests how we address patient consent. All health leaders can play a central role in advancing our application of data for system improvements. Strategies to support health leaders in achieving this potential are outlined in this article.
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 | 0.087 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".