Commentary – Equity, Diversity and Inclusion: A Key Solution to the Crisis of Doctoral Nursing Education in Canada
Bibliographic record
Abstract
Canada desperately needs more doctoral graduates. We also need more diverse graduates to move into education and leadership roles across the country. This article examines the origins and development of doctoral education for nurses in Canada and the continuing dire shortfall of doctorally prepared nurses to meet the expanding needs of the profession. In the context of this desperate shortage, this article then moves to examine the critical issues of equity, diversity and inclusion and the failure of the nursing academy and the profession to address these long-standing matters. These two issues - the shortfall of doctoral graduates and the lack of diversity in education and leadership in nursing - need to be addressed through a combined and focused strategy if we are to ensure the future sustainability of the profession. Given the decade-long lead time required to effect significant changes in doctoral graduations, the article concludes with a call for a national strategy engaging multiple stakeholders to increase awareness of the issues and their implications for the sustainability of the profession. It concludes that only through the united efforts of the profession will Canadian nursing be able to ensure that nursing education will produce a sufficient number of graduates for the needs of education, practice and policy across the country and that these graduates will better reflect the diversity of the nursing profession and the Canadian population, overall.
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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 | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.065 | 0.051 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".