Bibliographic record
Abstract
ing leaders to create a forum to address the enormous challenges that face the profession now, and will continue to do so over the next several years. Many in the profession have been addressing these challenges, typically within the confines of the organizations that employ them. There has been no focused forum where nurse leaders themselves have an intellectual space for dialogue and planning, for harnessing their collective experience and wisdom unfettered by organizational policy or politics. Collective leadership in times of crisis can offer an array of solutions that, if implemented cohesively, can make a significant difference. One of the purposes of ACEN is to provide such a forum. As a beginning step, the Academy of Canadian Executive Nurses has expanded its membership to include nurses in leadership positions across a wide variety of settings. Nurse executives from academic health organizations are still the core members of ACEN but welcome the inclusion of deans, research chairs and government and NGO nurse leaders. The response to this membership initiative has been very positive. Nurse leaders have expressed a strong interest and commitment to working together. The challenges facing nursing and healthcare in Canada are not unfamiliar to those in leadership positions. The greatest of these is the health human resource (HHR) crisis, which will intensify over the next decade. The shortage of healthcare professionals, particularly nurses and physicians, has been the subject of provincial and national reports, research programs and media releases. It has been the subject of many government and stakeholder meetings, and a topic of discussion over a period of three or four years for the federal/provincial/territorial Advisory Committee on Health Delivery and Human Resources (ACHDHR). First ministers agreed to coordinate efforts in the planning and management of HHR. Calling All Leaders
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.165 | 0.117 |
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".