Leadership for the Information Age: The Time for Action is Now
Bibliographic record
Abstract
Dr. Lynn Nagle, the senior nursing advisor for Canada Health Infoway, writes the column on nursing informatics for CJNL (Canadian Journal of Nursing Leadership). She and I have both been involved in the development and now the implementation of HOBIC (Health Outcomes for Better Information and Care), a province-wide initiative funded by the Ontario Ministry of Health and Long-Term Care: I am the executive lead and Lynn is the informatics lead. HOBIC seeks to bring online functionality to nurses that supports systematic assessment of patients on eight outcomes upon admission and discharge in acute care, chronic hospital care, long-term care and home care, and quarterly for people in residential settings. There is strong research evidence that nurses make a difference in how well patients do on these outcomes. Nurses can now access the results of their assessments online throughout a patient’s stay, compare them to other patients of similar age or gender and begin to set benchmarks for improving these outcomes. Unit managers and chief nursing officers receive an array of monthly reports on the admission and discharge status of patients – information that can also be reviewed on the basis of gender and age group. HOBIC will be a critical component of the electronic health record when it is wholly adopted throughout Ontario.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.087 | 0.034 |
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".