Bibliographic record
Abstract
Imagine a future of integrated clinical information systems that transcend the physical boundaries of clinical units, institutions and community care, providing nurses with comprehensive access to information and knowledge to support the delivery of care to individuals and families. Imagine not having to gather the same information repeatedly, ask the same questions over and over again, or struggle to assimilate information from multiple sources and informants. Better yet, as a person needing the services of the healthcare system, imagine not having to rely on memory for details of family health history or repeatedly provide the same information to numerous caregivers over the course of a single encounter (or multiple encounters) to satisfy the requirements of their specific data collection forms. The future lies in the electronic health record – but are we taking the right steps to get there? In particular, are we sufficiently challenging the status quo of the documentation structures associated with clinical information management?
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 | 0.272 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.031 | 0.062 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".