Bibliographic record
Abstract
This is the second in a series of reports to share key learnings from my sabbatical. In spring and summer of 2005, I took a three-month journey through Scandinavia, Europe, Ireland and the United Kingdom to observe innovation in nursing service delivery, in particular, nursing-led services; to explore outcome measurement as it relates to nursing services; to look at patient satisfaction and improving patients’ experience as a form of outcome measurement; to learn about palliative care; and to examine ways in which organizations, professional associations and policy makers are attempting to move nursing and healthcare services delivery into the future. I met with leaders in nursing and other health professions, policy makers, faculty and research units. During site visits, I spent time observing nurses at work. I visited teaching hospitals, district or community hospitals, community services, hospices and telehealth facilities. Our international colleagues extended a warm welcome, helped me gain exposure to things that might be of interest and were eager to learn about our practices in Canada. The focus of this report is patient safety, especially infection control and patient-centred care.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.011 | 0.032 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".