Forging a strong nursing future: insights from the Canadian context
Bibliographic record
Abstract
Canadian nurses, with their colleagues around the globe, are experiencing waves of change in their practice and work lives, and in expectations of how they will continue to make a difference for health and health care. We describe how Canadian nurses have been called to action to lead system wide changes in nursing practice, and to influence the wider public policy arenas for health. We aim to add to the growing international awareness of the status of nursing prompted by the Francis inquiry by offering our analysis of nursing practice and nursing leadership in Canada, in the context of the dominance of a managerial culture in health care systems. A review of prior commission reports, task forces and research reports sheds light on strategies needed to support nurses to address today’s challenges in nursing practice, including staff and skill mix determinations. We share our reflection on the current situation in Canadian nursing as a basis for learning about how our issues compare and contrast with others profiled in the journal. Our goal is to join with colleagues from Canada and other countries to forge a strong future – a future in which nursing’s voices are clearly heard in practice and policy decision-making, and where our knowledge and actions actualise societal health.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.085 | 0.023 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".