Bibliographic record
Abstract
McGrail, Zierler and Ip do an excellent job of analyzing the complex issues surrounding the value-for-money challenge in healthcare. In response to their call for a new perspective, the following observations are made. Many questions can be asked to help articulate values. More will be accomplished in the short and medium term by focusing on the simpler questions. Some questions about value will never have an absolute answer with complete agreement. Furthermore, what is valued in healthcare tends to be clouded by what is rewarded in healthcare. Although the authors call for reviving the notion of building a pan-Canadian health information strategy, there are excellent examples of provincial success stories on which to build (e.g., Ontario's Wait Times Information System). Research and evaluation will not add value unless they are closely linked to the knowledge needs of decision- and policy makers. In reply to the authors' call to stop treating information technology as optional and demand that anyone paid with public funds report on the use of those funds, it should be recognized that information technology is the enabler that everyone should use. What we need to stop treating as optional is accountability and appropriateness for the use of funds.
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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.075 | 0.074 |
| Insufficient payload (model declined to judge) | 0.007 | 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".