Bibliographic record
Abstract
Given predictions of a looming shortfall in nursing human resources and the need for strong leaders to meet the challenges of sustaining and managing the healthcare of Canadians in the face of this crisis, a focus on leadership development becomes critical. Canada needs a new cohort of leaders in nursing and healthcare, as the current cohort is fast approaching retirement. Today’s cohort of nursing leaders contains significantly fewer people than it did a decade ago owing to healthcare downsizing and cost containment. Those nurses who remain in leadership positions now cope with role expansion and increased competing demands, leaving little or no time to mentor emerging leaders in the profession. These facts, together with the lack of incentives to attract younger nurses into managerial and administrative positions, present a serious challenge for future nursing leadership. Recognizing the detrimental effects of the reduction in nurse leadership positions on the quality and efficiency of healthcare delivery and the retention of staff, several national reports, such as the Romanow (2002) Commission on the Future of Health Care and the First Ministers Health Accord (Government of Canada Privy Council Office 2003), have highlighted the importance of strengthening nursing leadership in Canada. The Canadian Health Services Research Foundation (2004) has identified the “nurturing of professional leaders” as a priority, along with the need to identify key attributes of successful leaders and specific competencies and skills required of leaders in healthcare. The CHSRF’s report, which was based on a national consultation process, also pointed to the need to describe effective approaches to the development of future healthcare leaders. The Academy of Canadian Executive Nurses recognizes its own commitment to the future of nursing leadership in Canada. As part of that commitment, ACEN is working in collaboration with Strengthening Mentorship for Leadership Development
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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 | 0.042 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".