Bibliographic record
Abstract
One of the major themes uncovered by Graham and Sibbald in their analysis of the 50-year-old issues of Hospital Administration in Canada (HAC) is the evolution of nursing. However, the HAC approach 50 years ago was that nursing was a problem to be solved, not a resource for health, the health system and the public, and that image would stay with nursing in Canada for many years to come. The recent commissioning by the Canadian Nurses Association of a National Expert Commission to examine sustainability of health and the healthcare system, and the resultant report, The Health of Our Nation, the Future of Our Health System: A Nursing Call to Action, released in June 2012, reflect a significantly different expectation about nurses and the nursing profession - they are not problems to be addressed, but are leading the solutions to better health, better care and better value. And patients are not passive recipients of care decided on by professionals alone, but central team members - "CEOs of their own healthcare" - in an inter-professional patient-/family-focused team that collectively supports people in their health journey. A number of examples of potential articles about and from nursing, based on the findings of the National Expert Commission, are included to illustrate how nursing should be reflected in an issue of HAC in 2012.
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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 | 0.044 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.044 |
| Scholarly communication | 0.031 | 0.044 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.025 | 0.043 |
| 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".